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Index: Evolution's Clinical Guidebook 11 May 2019 6:16 AM (5 years ago)

In the past few blogs, I've been discussing the recent publication of my book, Evolution’s Clinical Guidebook: Translating Ancient Genes Into Precision Medicine. The premise of this book is that modern medicine is based on an understanding of evolutionary processes. Evolution shows us the relationships between the subdisciplines of medicine that benefit directly from Precision Medicine (i.e., pathology, microbiology, clinical genetics, pharmacology, and bioinformatics). In Evolution's Clinical Guidebook, all of these diverse fields are brought together, under the subject of evolution. To illustrate, I have listed below the first few pages of the index to the book (letters A through H). Just by perusing these index terms, you can get some idea of the role played by evolution as the great unifier of modern medicine.

Partial Book Index

A 
Abiogenesis 
  catalysts, 2 
  cellular life, 
  earliest signs of, 4 
  definition, 1
  DNA, 4 
  evolution, 2 
  life on earth, 1 
  natural selection, 2
  RNA, 4­5
Acanthodians, 215
Acarnus erithacus, 192, 192f 
Acidianus Tailed Virus, 160
Acquired disease, 20, 29-­30
Actin, 17
Actinistia, 216
Actinopterygii, 215
Adaptive immune system, 214
Adaptive immunity, 161, 164
Adenocarcinoma, 125, 126-­127f Adult organisms, 94, 102
Agenesis of the Corpus Callosum (ACC), 222
Aging, 77, 216, 257-­258, 261-­265 
  vs. diseases of old people, 257-­259 
  evolution of, 252­-265 gene, 257­-258
Agnatha, 214
Allele, 77
Allium cepa, 66
Allium ursinum, 66 alphaA-Crystallin, 18
Alstrom syndrome, 213-­214
Alternative RNA splicing, 126
Amanita phalloides, 155
Amborella trichopoda, 150
Ambulacraria, 197
Amniotes, 218
Amoebozoa, 184, 290
Amphibia, 216­218
Amphioxus, 150
Amyloid world, 30
Anatomy, 246
Ancestral classes, 175-­176, 196­-197
Ancestral lineage, 12
Ancestral species, 12
  eukaryotic development, steps in, 14-­15, 15f 
  gene families, 13­-14
Ancylostoma duodenale, 292
Androgenesis, 221
Aneuploidy, 69­70, 70f Angelman syndrome, 222
Angiogenesis, 126
Angiosperma, 29­-44
Animal cells, 262
Animalia, 27f 
Animal model, human disease 
  Koch's postulates and reliance, 299-­300 
  nonhuman eutherians, 285 
  non-vertebrate models, cancer research, 298-­299 
  for orthodiseases
    Caenorhabditis elegans (nematodes), 294, 296-­298, 297f
    Danio rerio (zebrafish), 294, 297-­298, 298f 
    Drosophila melanogaster (fruit fly), 294, 297 
  homologous genes, 296 
  human pathologic processes, 295 
  orthologous genes, 295­-296
  Saccharomyces cerevisiae (yeast), 294­-296, 296f 
  rabbits, myxoma virus on, 300-­302 
  rats, 285 
  specificities and idiosyncrasies
    clinical trial, 286­-287
    Gram-negative organisms, 286 
    infections, history of, 290­-291, 293-­294 
    inflammatory response, 287 
    lipopolysaccharide, 286 
    mice, 286­288 
    microorganisms, potential pathogens, 288-­290 
    rodent models, 287-­288
  TGN1412, 286­287
Anlagen, 127
Aplastic anemia, 270
Apoikozoa, 186­-187
Apomorphy, 198
Apoptosis, 53
Archaea, 26, 27f 
Archaeplastida, 14-­15, 28, 186
Archiplastidae, 185
Arrhythmogenic cardiomyopathies, 191
Arthropods, hepatopancreas of, 118-­119
Ascaris lumbricoides, 292
Aspergillus flavus, 155
Association vs. cause, 77
Ataxia telangiectasia, 30
Atlantogenata, 229
Autism, 58
Autoantibody disease vs. autoimmune disease, 233
Autosomal dominance, 270
Azacytidine, 122

B 
Bacillus globigii, 288
Bacillus subtilis, 244
Bacteria, 26
Baraitser-Winter syndrome, 57
Bartonella species, 26
Basal cell carcinomas, 223-­224, 223f Basal layer, 255
Benign tumor, 218, 233
Bikonta, 183­184
Bilateria, 94, 193­203, 254
Bioinformatics, 78
Biological diversity, 152
Biological theory, 308
Biopsy specimen, 255f Biosphere, 157
Biosynthetic cycle, 17
BK polyomavirus, 292
Blastocystis hominis, 289
Blastula, 191
Blastulation, 189, 190f Blended class, 322, 325
Blood, photomicrograph of, 108f Bloom syndrome, 262
Bone marrow, 256
Bookie, 266
Bootstrapping paradoxes, 5
  chicken and egg paradox, 6­8 
  enzyme and enzyme-synthesizing machinery, 8 
  general solution for, 11-­12 
  hardware or software, 5­6 
  process of evolution and product of evolution, 9-­10
  RNA and DNA, 8­9, 10f 
  species and class of animals, 10-­11
Borderland of Embryology and Pathology, 118-­120
Boreoeutheria, 229
Borhing-Opitz syndrome, 57
Brassica oleracea, 158
BRCA, 271
BRCA1 gene, 269
BRCA2 gene, 269
Breast cancer, 260
Breeds, 248, 249f Brugia malayi, 292
Bryophyte life cycle, 7
Bryophytes, 7
BUB1B gene, 70
Bubonic plague, 292
Bungarus caeruleus, 110f "But-for" test, 30

C 
Caenorhabditis elegans, 65, 123, 294, 296-­298, 297f Calvin cycle, 17
Cambrian explosion, 21­-25, 21­-22f, 155-­156, 185 
  coexistence and coevolution, 25­26
  animals (class Metazoa), 28
  Archaea, 26, 27f 
  Archaeplastida (plants), 28 
  bacteria, 26 
  fungi, 28 
  single-celled eukaryotes, 28 
  viruses, 26
Cambrian period, 21-­22, 22f, 24
Cancer, 259­261
Cancer cells, 51
Cancer progression, 30
Carcinogen, 127
Carcinogenesis, 17, 30-­31, 223
Carcinoid tumors, 211
Carcinosarcomas, of uterus, 119
Caretaker diploid organism, 7
Carotenoids, 156
Carrier, 26, 31 asymptomatic, 27
Catarrhini, 231
Cause, 19, 24, 28
Cell types, epigenome and evolution of, 103-­115
Cell-type-specific gene expression, 112
Cenancestor, 37
Cephalochordata, 198­-203
Cercopithecoidea, 231
Chagas disease, 28, 269-­270
Chance occurrence, 9­10, 31
Channelopathy, 127
Charcot-Marie-Tooth disease, 261
CHARGE syndrome, 57
Chemical diversity, 154
Chemokine, 293, 301
Child class, 198
Chimeric Antigen Receptor for T cells (CAR-T) therapy, 157, 164
Chitin, 185
Chlamydia trachomatis, 292
Chloroplast evolution, 14­15, 17, 31
Choanoflagellatea, 186­187
Choanozoa. See Apoikozoa Chondrichthyes, 215
Chordata, 197­203
Chordoma, 198
Choriocarcinoma, 221
Chromatin, 156
Chromosomal disorder, 271
Chromosomes, 61
  number, variations in, 66
Chronic obstructive pulmonary disease (COPD), 271
Chytrids, 186
Cichlids, 153­154, 154f Ciliopathies, 213-­214, 233
Cis-acting vs. trans-acting, 127-­128
CISD2 gene, 264
Cisd2-null mice, 264
Clade, 128
Cladistics, 198
Class, 7, 32
  of animals, 10, 12, 24­-25 
  of cells, 6 
  of metazoan organisms, 25 
  of organisms, 4­5 
  of paradoxes, 5
Classification, 11 
  data retrieval, 176 vs. diagnosis, 199 
  flying animals, 175 
  formal definition of, 175 
  inferencing, 176 
  mammals, Aristotle, 173-­174 vs. ontology, 177, 198 
  pseudo-scientific assertion, 177 
  self-correction, 177 
  simplification, 175 swimming animals, 175 walking animals, 175
Classification system vs. identification system, 325
Class noise, 322. See also Blended class Clinical trial, 286-­287, 301
Clostridium feseri (blue bacteria), 107f 
Clustered Regularly Interspaced Short
  Palindromic Repeats (CRISPR), 251
Cnidaria, 193
Cnidarian organisms, 193, 195f Coccidia, 28
Cockayne syndrome, 262
Codon, 8, 32
Cofactor, 156, 165
Collision tumor theory, 120
Colon cancers, 119
Combined deficiency, 128
Commensal, 32­33
Competence of classification, 176
Complex disease, 33
Composition theory, 120
Congenital anomaly, 271
Congenital chondrodystrophy, 17-­18
Congenital disorder, 17-­18, 33
Congenital hemangiomas, 119
Connective tissue, 128
Contig disease, 271
Contiguous gene deletion syndrome, 271
Convergence, 165
Conversion theory, 120
Copy number, 78
Corbels, 239-­240
Cornelia de Lange syndrome, 112
Corpus callosum, 222
Cousin class, 167
CpG island, 128
CpG sites, 103
Cranial neural crest, 208
Craniata, 128, 148-­149, 198­-203, 207-­212
Craniates. See Craniata Crocodilia, 218
Crohn's disease, 273
Ctenophora, 193
Ctenophorans, 193, 194f Cyanobacteria, 14­15, 15f, 17, 23, 33
Cyclic neutropenia, 55
Cyclostomata, 211-­212
Cynodonts, 219
Cystic fibrosis transmembrane conductance regulator (CFTR), 113
Cytokine storm, 286-­287
Cytopenia, 78
Cytotrophoblasts, 229


D 
Danio rerio (zebrafish), 294, 297­-298, 298f Daphnia pulex, 154
Darwin's theory, 153
Decitabine, 122 delta1-Crystallin, 18
Demodex, 291
Demospongiae, 192
De novo disease mutations, 56-­58
De novo genes, 74­-76
De novo mutation, 78
Dense core granules, 210
Dermal bones, 209
Dermis, 255
Dermoptera, 230
Desmosomes, 187, 188f, 189, 190f, 191
Deuterostomia, 197
Deuterotomia, 148-­149
Developmental disorder, 128
Devolution, 241
Diagnosis vs. classification, 199
Diamond Blackfan anemia, 210
Diethystilbestrol (DES), 117
Differentiation, 78
Digenic disease, 128­-129
DiGeorge syndrome, 82
Dinosauria, 218
Diploid organism, 7
Dipnomorpha, 216
Dipnotetrapodomorpha, 216
Dipoblasts, 193
Direct mutagen, 52
Direct transdifferentiation, 111
DNA, 73­87
DNA-DNA reassociation kinetics, 151-­152
DNA methylation, 4, 33
DNA repair, 17, 33
Dollo's law, 271
Dormancy, 33
Down syndrome, 56
Driver pathway, 19, 34
Drosophila melanogaster, 101, 294, 297
Drug development, economics of, 20
Druggable driver, 34
Dysgerminomas in women, 102
Dyskeratosis congenita, 262
Dysplasia, 199


E 
Echidnas, 226
Echinodermata, 197
Ectoderm, 129
Eikenella corrodens, 289
Embryo, 99­103 vs. fetus, 129
Embryogenesis, 161
Embryology, relationship between evolution and, 93-­103
Embryonic anlagen, 102
Embryonic stem cell, 129
Endoderm, 6, 34
End-stage condition, 272
Enhancer, 78
Enigmatic pacific hagfish, 211-­212, 212f 
Enterocoelomata. See Deuterostomia Epidermis, 255, 256f 
Epigenome, 4, 34, 221
Epigenome disruptors, 121-­122
Epigenomic methylation, variations in, 65
Epimutation, 129-­130
Epipubic bones, 239
Epistasis, 34­35
Epithelial cell, 130
Epitheliozoa, 193
Epithelium, 189, 189f Erasure, 105-­106, 221
ERCC6 gene, 262
ERCC8 gene, 262
Etiology, 79
Euarchonta, 230
Euarchontoglires, 229-­230
Eugenics, 247-­252
Eugnathostomata, 215
Eukaryota, 15, 148­149
  Bikonta, 183-­184 eukaryotes, 179
  Excavata, 183-­184 
  mitochondria, 180 
  nucleus, 179, 182
  Podiata, 183­184 
  prokaryotic life forms, 179 
  single-celled eukaryotes, 179, 180f 
  Syringammina fragilissima, 179 
  undulipodia, 182
  Unikonta, 183-­184
Eukaryotes, 7, 67, 153­-155, 161, 176, 189
Eumetazoa, 191-­193
Euteleostomi, 215
Eutheria, 97-­98, 226-­235
Eutherians, 226-­235
Evo-devo, 130
Evolutionary convergence, 166
Evolutionary frustration, principle of, 248
Evolution, 
  as fantasy bacterial pathogen, 317­-319 
  disease diagnosis by symptoms, 321­-323 
  drug development and testing, 319 
  homologous genes, 316­317 
  science fiction aficionados, 324 
  speciation, 324 
  taxonomic organisms, treatments for, 319-­321 
  theory of intelligent design, 324-­326
Evolution deniers, 307
Evolvability, 153, 165
Exaptation, 35
Excavata, 183­184, 290
Exome sequencing, 79
Extraembryonic cells, 233
Extraembryonic tissues, 221, 233


F 
Facultative intracellular organism, 79
Fanconi anemia bone marrow failure, 257, 263
Female Anopheles mosquito, 107
Fetal period, 101
Filarial nematodes, 292
Filozoa, 186
First Law of Bioinformatics, 59
Fish, 118-­119
Forme fruste, 130
Founder effect, 13, 35
FOXL2 gene, 112
Fungi, 28, 186, 189


G 
Gallertoids, 130, 187, 191­192
Gametes, 6, 35, 148­-149
Gametic organism, 7
Gametophytes, 7
Gastrointestinal stromal tumors (GISTs), 19
Gastropods, 118-­119
Gene(s), 269
Gene conservation, 58-­61
Gene diversity, 154
Gene editing techniques, 251
Gene pool, 13, 26, 35, 58­61, 151­152, 314, 317­319, 324
Generalization, 35
Gene regulation, 35
Gene sharing, 18
Gene size, 66­67
Gene-targeted therapy, 19
Genetically engineered mouse (GEM), 233-­234
Genetic fine-tuning, 124­-138
Genetic heterogeneity, 130
Genetic instability, 79
Genetic mutations, 151
Genetic surplus disorder, 79
Genome, 12, 14-­15, 26, 35
Genome Wide Association Study (GWAS), 272
Genomic architecture, 64­73
Genomic disorder, 79
Genomic regulation, 76­87
Genomic regulatory processes, 125­138
Genomic regulatory systems, 
  pathologic conditions of, 121-­138
Genomic structural abnormalities (GSVs), 69
Genomic structural variation, 79
Germ cell, 6, 35
Germ cell line, 35­-36
Germ layers, 131
Germline, 79
Germline mutation, 80
Gestational trophoblastic disease, 234
Giant viruses, 162
Glires, 230
Globins, 13
Gnathostomata, 161, 214-­215
Gorillini, 232


H 
HACEK, 272
Haeckel's theory, 96-­97
Haemophilus influenzae, 244
Hair follicles, 223­224, 224f Hamartoma, 158, 166
Haploid, 7­8, 36
Haploid organisms, 7, 36
Haplorrhini/haplorhini, 231
Haplotype, 80
HAS2 gene, 248
Hemichordata, 197
Hepatitis B, 292
Hepatocyte, 100
Hepatoid adenomas, 119
Hepatoma, 131
Hereditary nonpolyposis colorectal cancer syndrome, 131
Heritability, 131
Heterokonts, 289, 301
Hirschspring disease, 209
Histone, 65, 199
Histone disruptors, mild effects of, 122
Histopathology, 131
Histozoa, 193
Hodgkin lymphoma, 273
Holometabolism, 156, 166
Holomycota, 186
Holozoa, 186
Holt-Oram syndrome, 123
Homeobox, 36
Hominidae, 231­232
Homininae, 232
Hominini, 232
Hominoidea, 231
Homo, 232
Homo erectus, 232
Homolog, 12, 18, 24, 36
Homologous genes, 316-­317
Homologous recombination, 
  during meiosis, 62
Homoplasy, 166
Homo sapiens, 147­149, 176, 232­235
Homozygosity, 80
Hookworms, 292
Horizontal gene transfer, 80
Horse, 
  gestation period of, 241f 
Horseshoe crabs, 149, 149f 
Host, 155­157, 159­161, 163, 166
HOX gene diseases, mild clinical course of, 123
HOX genes, 24
Human(s), 232­235
Human diseases, 115­118
Human embryo, dorsum of, 97f Human embryology, 246
Human embryonic stem cells, 100f Human gene pool, 55-­56
Human kidney, 101
Human phylogenetic lineage, 177­-179
Hutchinson-Gilford progeria syndrome, 263
Hydatidiform mole, 221, 234
Hydractinia carnea, 253
Hydrops-ectopic calcification-"moth-eaten" (HEM), 17-­18
Hylobatidae, 231
Hyperplasia, 273
Hypoxanthine-guanine phosphporibosyl transferase (HGPRT), 60­-61

Evolution’s Clinical Guidebook: Translating Ancient Genes Into Precision Medicine is available from Amazon or from the publisher's website. If you are fortunate enough to have full institutional access to ScienceDirect, you can download chapters at no cost.

Jules Berman



key words: evolution, precision medicine, genetics, rare disease, clinical genetics, bioinformatics, evo-devo, Jules J. Berman, Ph.D., M.D.

Contents: Evolution’s Clinical Guidebook: Translating Ancient Genes Into Precision Medicine 10 May 2019 6:40 AM (5 years ago)

In yesterday's blog, I announced the publication of my book, Evolution’s Clinical Guidebook: Translating Ancient Genes Into Precision Medicine. The premise of this book is that modern medicine is based on an understanding of evolutionary processes. Basically, without evolution, the fledgling field of precision medicine would wither and die, and we would lose our opportunity to prevent, diagnose, and treat the diseases that account for the bulk of morbidity and mortality in humans and in animals.

This book is available from Amazon or from the publisher's website. If you are fortunate enough to have full institutional access to ScienceDirect, you can download chapters at no cost. Here is the Table of Contents.

Contents: Evolution’s Clinical Guidebook: Translating Ancient Genes into Precision Medicine

1. Evolution, From the Beginning 1

Section 1.1 In the Beginning 1
Section 1.2 Bootstrapping Paradoxes 5
Section 1.3 Our Genes, for the Most Part, 
Come From Ancestral Species 12
Section 1.4 How do Metabolic Pathways Evolve? 15
Section 1.5 Cambrian Explosion 21
Section 1.6 After the Cambrian: Coexistence 
and Coevolution 25
Glossary 29
References 44

2. Shaking Up the Genome 51
Section 2.1 Mutation Burden 51
Section 2.2 Gene Pools and Gene Conservation 58
Section 2.3 Recombination and Other 
Genetic Tricks 61
Section 2.4 Genomic Architecture: 
An Evolutionary Free-for-All 64
Section 2.5 Rummaging Through the DNA Junkyard 73
Glossary 77
References 87

3. Evolution and Embryonic Development 93
Section 3.1 The Tight Relationship Between 
Evolution and Embryology 93
Section 3.2 The Epigenome and the Evolution 
of Cell Types 103
Section 3.3 An Embryonic Detour for 
Human Diseases 115
Section 3.4 The Borderland of Embryology 
and Cancer 118
Section 3.5 Pathologic Conditions of the 
Genomic Regulatory Systems 121
Glossary 125
References 138

4. Speciation 145
Section 4.1 A Species is a Biological Entity 145
Section 4.2 The Biological Process of Speciation 147
Section 4.3 The Diversity of Living Organisms 152
Section 4.4 The Species Paradox 157
Section 4.5 Viruses and the Meaning of Life 159
Glossary 164
References 168

5. Phylogeny: Eukaryotes to Chordates 173
Section 5.1 On Classification 173
Section 5.2 The Complete Human 
Phylogenetic Lineage 177
Section 5.3 Eukaryotes to Obazoans 179
Section 5.4 Opisthokonts to Parahoxozoa 185
Section 5.5 Bilaterians to Chordates 193
Glossary 198
References 203

6. Phylogeny: Craniates to Humans 207
Section 6.1 Class Craniata and the Ascent 
of the Neural Crest 207
Section 6.2 Vertebrates to Synapsids 212
Section 6.3 Mammals to Therians 220
Section 6.4 Eutherians to Humans 226
Glossary 233
References 235

7. Trapped by Evolution 239
Section 7.1 Spandrels, Pendentives, Corbels, 
and Squinches 239
Section 7.2 Evolving Backwards 240
Section 7.3 Eugenics: Proceed With Caution 247
Section 7.4 The Evolution of Aging, 
and the Diseases Thereof 252
Section 7.5 Why Good People Get Bad Diseases 265
Glossary 270
References 277

8. Animal Models of Human Disease: Opportunities 
and Limitations 285
Section 8.1 The Animal Model Problem, in a 
Nutshell 285
Section 8.2 Specificities and Idiosyncrasies 286
Section 8.3 New Animal Options 294
Section 8.4 The Proper Study of Mankind 300
Glossary 301
References 302

9. Medical Proof of Evolution 307
Section 9.1 What Does Proof Mean, 
in the Biological Sciences? 307
Section 9.2 The Differences Between Designed 
Organisms and Evolved Organisms 309
Section 9.3 What if Evolution Were Just 
a Foolish Fantasy 316
Glossary 325
References 326
Index 329

Jules Berman



key words: evolution, precision medicine, genetics, rare disease, clinical genetics, bioinformatics, evo-devo, Jules J. Berman, Ph.D., M.D.

Just Published: Evolution’s Clinical Guidebook: Translating Ancient Genes Into Precision Medicine 9 May 2019 1:17 PM (5 years ago)

This month, Academic Press has published my book, Evolution’s Clinical Guidebook: Translating Ancient Genes Into Precision Medicine. The premise of this book is that modern medicine is based, in one way or another, on an understanding of evolutionary processes. If evolution were a fabrication, then we would not be able to make any sense of the genomic data that is pouring out of research laboratories. We would not be able to design rational, cost effective, screening protocols to test the effectiveness of new drugs. We would not be able to identify the human sub-populations that will benefit from gene-targeted therapies. We would not be able to find the cause of rare diseases, and we would not be able to apply such knowledge to the treatment of common diseases. Without evolution, we would not understand how cancer develops, or how we might intervene in the process. Basically, without evolution, the fledgling field of precision medicine would wither and die, and we would lose our opportunity to prevent, diagnose, and treat the diseases that account for the bulk of morbidity and mortality in humans and in animals. This book demonstrates, through hundreds of examples, that modern medicine is built on the theory of evolution.

This book is available from Amazon or from the publisher's website. If you are fortunate enough to have full institutional access to ScienceDirect, you can download chapters at no cost.

Jules Berman

key words: evolution, precision medicine, genetics, rare disease, clinical genetics, bioinformatics, evo-devo, Jules J. Berman, Ph.D., M.D.

Second Edition of Principles and Practice of Big Data now on Science Direct 4 Aug 2018 7:54 AM (6 years ago)

The Second edition of my book Principles and Practice of Big Data has just been released and is available for purchase at many sites, including Amazon.

For those of you fortunate enough to have access to Science Direct, you can download chapters of my book at:

https://www.sciencedirect.com/science/book/9780128156094

TABLE OF CONTENTS

  Author's Preface to Second Edition 

  Author's Preface to First Edition 

  Chapter 1. Introduction
    Section 1.  Definition of Big Data
    Section 2.  Big Data Versus small data
    Section 3.  Whence Comest Big Data?
    Section 4.  The Most Common Purpose of Big Data is to Produce small data
    Section 5.  Big Data Sits at the Center of the Research Universe
    Section 6.  Case Study: From the Press: Big Claims for Big Data

  Chapter 2. Providing Structure to Unstructured Data
    Section 1.  Nearly all Data is Unstructured and Unusable in its Raw Form
    Section 2.  Term Extraction
    Section 3.  Autocoding
    Section 4.  Concordances
    Section 5.  Indexing
    Section 6.  Machine Translation
    Section 7.  Case Study: Sorted Lists (Why and Why Not)
    Section 8.  Case Study: Doublet Lists 
    Section 9.  Case Study: Ngram Lists 
    Section 10.  Case Study: Proximity Searches Using Only a Concordance  
    Section 11.  Case Study (Advanced): Burrows Wheeler Transform (BWT) 

  Chapter 3. Identification, Deidentification, and Reidentification
    Section 1.  What are Identifiers?
    Section 2.  Difference Between an Identifier and an Identifier System
    Section 3.  Generating Identifiers
    Section 4.  Really Bad Identifier Methods
    Section 5.  Registered Unique Object Identifiers
    Section 6.  Deidentification
    Section 7.  Reidentification
    Section 8.  Case Study: Data Scrubbing
    Section 9.  Case Study: Identifiers in Image Headers
    Section 10.  Case Study: Hospital Registration
    Section 11.  Case Study: One-Way Hashes

  Chapter 4. Metadata, Semantics, and Triples
    Section 1.  Metadata
    Section 2.  eXtensible Markup Language
    Section 3.  Namespaces
    Section 4.  Semantics and Triples
    Section 5.  Case Study: Syntax for Triples 
    Section 6.  Case Study: RDF Schema
    Section 7.  Case Study: RDF Parsers and the Fungibility of Triples
    Section 8.  Case Study: Dublin Core 

  Chapter 5. Classifications and Ontologies
    Section 1.  It's All About Object Relationships 
    Section 2.  The Difference Between Object Relationships and Object Similarities
    Section 3.  Classifications, the Simplest of Ontologies
    Section 4.  Ontologies, Classes with Multiple Parents
    Section 5.  Choosing a Class Model
    Section 6.  Paradoxes
    Section 7.  Class Blending
    Section 8.  Common Pitfalls in Ontology Development
    Section 9.  Case Study: An Upper Level Ontology 
    Section 10.  Case Study: Visualizing Class Relationships 
    Section 11.  Case Study: Bringing Order from Chaos with the Classification of Living Organisms

  Chapter 6. Introspection
    Section 1.  Knowledge of Self
    Section 2.  Data Objects
    Section 3.  How Big Data Uses Introspection 
    Section 4.  Case Study: Timestamping Data 
    Section 5.  Case Study: A Visit to the TripleStore 

  Chapter 7. Data Integration and Software Interoperability
    Section 1.  Another Big Problem for Big Data
    Section 2.  The Standard for Standards
    Section 3.  Standard Trajectories
    Section 4.  Specifications and Standards
    Section 5.  Versioning
    Section 6.  Compliance Issues
    Section 7.  Interfaces to Big Data Resources
    Section 8.  Case Study: Standardizing the Chocolate Teapot

  Chapter 8. Immutability and Immortality
    Section 1.  The Importance of Data that Cannot Change  
    Section 2.  Immutability and Identifiers
    Section 3.  Persistent Data Objects
    Section 4.  Coping with the Data that Data Creates
    Section 5.  Reconciling Identifiers Across Institutions
    Section 6.  Case Study: The Trusted Timestamp
    Section 7.  Case Study: Blockchains and Distributed Ledgers
    Section 8.  Case Study: Zero-Knowledge Reconciliation   

  Chapter 9. Assessing the Adequacy of a Big Data Resource
    Section 1.  Looking at the Data 
    Section 2.  The Minimal Necessary Properties of Big Data 
    Section 3.  Case Study: Utilities for Viewing and Manipulating Very Large Files
    Section 4.  Case Study: Flattened Data 
    Section 5.  Case Study: Data that Comes with Conditions 

  Chapter 10. Measurement
    Section 1.  Accuracy and Precision
    Section 2.  Data Range
    Section 3.  Counting
    Section 4.  Normalizing, and Transforming Your Data
    Section 5.  Reducing Your Data
    Section 6.  Understanding Your Control
    Section 7.  Practical Significance of Measurements
    Section 8.  Case Study: Gene Counting
    Section 9.  Case Study: The Significance of Narrow Data Ranges
    Section 10.  Case Study (Advanced): Fast Fourier Transform
    Section 11.  Case Study (Advanced): Principal Component Analysis

  Chapter 11. Indispensable Tips for Fast and Simple Big Data Analysis
    Section 1.  Speed and Scalability
    Section 2.  Fast Operations, Suitable for Big Data, that Every Computer Supports
    Section 3.  Fast Correlation Methods
    Section 4.  Clustering 
    Section 5.  Methods for Data Persistence (Without Using a Database)
    Section 6.  Back_of_Envelope Computations for Big Data
    Section 7.  Fast Data Retrieval for Lists of any Size 
    Section 8.  Case Study: One-Pass Mean and Standard Deviation
    Section 9.  Case Study: Climbing a Classification
    Section 10.  Pre-computing lookup lists: Google's PageRank
    Section 11.  Case Study: A Database Example 
    Section 12.  NoSQL and other Non-Relational Big Data Databases

  Chapter 12. Finding the Clues in Large Collections of Data
    Section 1.  Denominators 
    Section 2.  Frequency Distributions
    Section 3.  Multimodality
    Section 4.  Outliers and Anomalies
    Section 5.  Case Study: Discarding the Noisiest Frequencies in a Data Signal
    Section 6.  Case Study: Predicting User Preferences
    Section 7.  Case Study: Multimodality in Legacy Data
    Section 8.  Case Study: Big and Small Black Holes

  Chapter 13. Using Random Numbers to Your Big Data Analytic Problems Down to Size
    Section 1.  The Remarkable Utility of (Pseudo)Random Numbers 
    Section 2.  Resampling and Permutating 
    Section 3.  Case Study: Sample Size and Power Estimates
    Section 4.  Monte Carlo Simulations
    Section 5.  Case Study: Monty Hall Problem: Solving What We Cannot Grasp
    Section 6.  Case Study: Frequency of Unlikely String of Occurrences 
    Section 7.  Case Study: The Infamous Birthday Problem
    Section 8.  Case Study: A Bayesian Analysis of Insurance Costs 

  Chapter 14. Special Considerations in Big Data Analysis
    Section 1.  Theory in Search of Data 
    Section 2.  Data in Search of Theory
    Section 3.  Overfitting
    Section 4.  Bigness Bias
    Section 5.  Too Much Data
    Section 6.  Fixing Data
    Section 7.  Data Subsets in Big Data: Neither Additive nor Transitive
    Section 8.  Additional Big Data Pitfalls
    Section 9.  Case Study: Curse of Dimensionality

  Chapter 15. Big Data Failures and How to Avoid (Some of) Them
    Section 1.  Failure is Common
    Section 2.  Failed Standards
    Section 3.  Blaming Complexity
    Section 4.  Perils of Redundancy
    Section 5.  Save Time and Money; Don’t Protect Data that Does not Need Protection
    Section 6.  An Approach to Big Data that May Work For You
    Section 7.  After Failure
    Section 8.  Case Study: Cancer Biomedical Informatics Grid, a Bridge too Far
    Section 9.  Case Study: The Gaussian Copula Function

  Chapter 16. Legalities
    Section 1.  Responsibility for the Accuracy and Legitimacy of Data
    Section 2.  Rights to Create, Use, and Share the Resource
    Section 3.  Copyright and Patent Infringements Incurred by Using Standards
    Section 4.  Protections for Individuals
    Section 5.  Consent
    Section 6.  Unconsented Data
    Section 7.  Good Policies are a Good Policy
    Section 8.  Case Study: The "Inconclusive" Data Analysis
    Section 9.  Case Study: The Havasupai Story
    Section 10.  Case Study: Double-edged Sword of the U.S. Data Quality Act 

  Chapter 17. Data Sharing 
    Section 1.  What Is Data Sharing, and Why Don't We Do More of It?
    Section 2.  Common Complaints
    Section 3.  Case Study: Life on Mars
    Section 4.  Case Study: Who Shares Their Data 
    Section 5.  Case Study: National Patient Identifier

  Chapter 18. Data Reanalysis: Much More Important than Analysis
    Section 1.  First Analysis (Nearly) Always Wrong 
    Section 2.  Why Reanalysis is More Important than Analysis
    Section 3.  Case Study: Reanalysis of Old JADE Collider Data 
    Section 4.  Case Study: Vindication Through Reanalysis 
    Section 5.  Case Study: Finding New Planets from Old Data 

  Chapter 19. Repurposing Big Data
    Section 1.  What is Data Repurposing? 
    Section 2.  Dark Data, Abandoned Data, and Legacy Data 
    Section 3.  Case Study: From Postal Code to Demographic Keystone 
    Section 4.  Case Study: Fingerprints and Data-driven Forensics
    Section 5.  Scientific Inferencing from a Databases of Genetic Sequences
    Section 6.  Case Study: Linking global warming to high-intensity hurricanes
    Section 7.  Case Study: Inferring climate trends with geologic data
    Section 8.  Case Study: Old tidal data, and the iceberg that sank the Titanic
    Section 9.  Case Study: Lunar Orbiter Image Recovery Project
    Section 10.  Case Study: The Cornucopia of the Natural Sciences

  Chapter 20. Societal Issues
    Section 1.  How Big Data Is Perceived by the Public
    Section 2.  Reducing Costs and Increasing Productivity with Big Data
    Section 3.  Public Mistrust
    Section 4.  Saving Us from Ourselves 
    Section 5.  Who is Big Data?
    Section 6.  Hubris and Hyperbole
    Section 7.  Case Study: The Citizen Scientists
    Section 8.  Case Study: 1984, by George Orwell

  

- Jules Berman

Read Precision Medicine and the Reinvention of Human Disease on ScienceDirect 9 May 2018 2:40 PM (6 years ago)

It is regrettable that many of my textbooks are unaffordable to the majority of the potential market. For Example, Precision Medicine and the Reinvention of Human Disease sells on Amazon for $125. This book contains nearly a quarter-million words, and it must have cost the publisher a lot of money to print and distribute, but I certainly wish it could have been sold at a lower price.

As a remedy, for some of you, this book is being marketed by Elsevier (the owner of the Academic Press imprint under which is was published) through ScienceDirect, a subscription online book catalog bought by university libraries. This means that if you have online access to a university library that has paid for a ScienceDirect subscription, you may have free access to my book.

Precision Medicine and the Reinvention of Human Disease was published January 30, 2018, and it is possible that your university library may have a ScienceDirect subscription that does not yet access my book. After speaking today with my editor, it's my impression that ScienceDirect access for libraries is something akin to cable channel access for homes. You can add access to specific books or you can add access to bundles of books that cover areas of interest. If you have access to ScienceDirect, but your university doesn't yet have access to my book(s), please talk to your librarian and ask if he/she will add my Elsevier publications to their ScienceDirect subscription.

There is an excellent preview of Precision Medicine and the Reinvention of Human Disease at the Google books site.

- Jules Berman

key words: precision medicine, ScienceDirect, library acquisitions, book subscriptions, jules j berman Ph.D. M.D.

Inscrutable Genes 15 Feb 2018 4:19 AM (7 years ago)

• "In most cases, the molecular consequences of disease, or trait-associated variants for human physiology, are not understood." from: Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature 2009;461:747–53.

The 1960s was a wonderful decade for the field of molecular genetics. Hundreds of inherited metabolic diseases were being studied. Most of these diseases could be characterized by a simple inherited mutation in a disease-causing gene. Back then, we thought we understood genetic diseases. Here’s how it all might have worked, if life were simple: one mutation! one gene ! one protein ! one disease. This lovely genetic parable, from a bygone generation, seldom applies in the era of Precision Medicine. The purpose of this section is to explain some of the complexities of modern genetics and to lay out the job of the Precision Medicine scientist who must dissect the pathways that lead from gene to disease.

In Precision Medicine and the Reinvention of Human Disease, two of the most confuding aspects of modern disease genetics are discussed: that a single disease may result from one of many distinct molecular defects; and that a single gene may produce many different diseases. These two countervailing phenomena tell us something very important about disease development. The first is that different pathways may converge to the same disease, and that any single gene may perturb a biological system (i.e., a living organism) in different ways. Some of that discussion is excerpted here.

There are numerous examples wherein mutations in one gene may result in more than one disease [2]. In some cases, each of the diseases caused by the altered gene is fundamentally similar (e.g., spherocytosis and elliptocytosis, caused by mutations in the alpha-spectrin gene; Usher syndrome type IIIA and retinitis pigmentosa-61 caused by mutations in the CLRN1 gene). In other case, diseases caused by the same gene may have no obvious relation to one another. For example, the APOE gene encodes apolipoprotein E, which is involved in the synthesis of lipoproteins. One common allele of the APOE locus, e4, increases the risk of Alzheimer disease and of heart disease, two disorders of no obvious clinical similarities [3,4].

Let’s look at a few other examples where mutations in a single gene play causal roles in the development of diverse diseases. For example, different mutations of the same gene, desmoplakin, cause the following diseases [2]:

• Arrhythmogenic right ventricular dysplasia 8

• Dilated cardiomyopathy with woolly hair and keratoderma

• Lethal acantholytic epidermolysis bullosa

• Keratosis palmoplantaris striata II

• Skin fragility-woolly hair syndrome

How is it possible that errors in the gene coding for desmoplakin, a constituent protein found in intercellular junctions, could account for such apparently unrelated diseases as arrhythmogenic right ventricular dysplasia and lethal acantholytic epidermolysis bullosa? It happens that we know that specialized desmosomes in cardiac cells (i.e., intercalated discs) tightly couple myocytes so that they can function as a coordinated group. Desmosomes are also required to adhese skin epidermal cells to one another and to the underlying basement membrane. In the case of desmoplakin mutations, it is relatively easy to see the pathogenetic relationship among these diseases.

In other sets of diseases that result from an error in one specific gene, the pathogenetic relationship may not be so easily discerned. Some cases of Charcot-Marie-Tooth axonal neuropathy, lipodystrophy, Emery-Dreyfus muscular dystrophy, and premature aging syndromes are all caused by mutation in the LMNA (Lamin A/C) gene. Stickler syndrome type III, Fibrochondrogenesis-2, and a form of nonsyndromic hearing loss are all caused by mutations in the COL11A2 gene. In these cases, how can variations in a single gene cause many different diseases?

Let’s look at just a few of the possibilities:

• One gene can control the synthesis of more than one protein [6].

• A single protein may have multiple functions. For example, nuclear lamina (lamin a/c) has several biological roles: controlling nuclear shape, influencing transcription, and organizing heterochromatin. Mutations in the LMNA gene cause more than 10 different clinical syndromes, including neuromuscular and cardiac disorders, premature aging disorders, and lipodystrophy. Likewise, the polyfunctional TP53 gene has been linked to 11 clinically distinguishable cancer-related disorders [7].

• A single protein with a single function may have different biological effects based on the cell type in which the protein is expressed, the stage of development in which the protein is expressed, and the cellular milieu (e.g., concentrations of substrate or protein inhibitors) for a given cell type, at a particular moment in time.

• Diseases develop through a sequence of biological events occurring over time. A mutation may exert a different biological effect based on where and when, in the sequence of pathogenetic events, it is expressed.

more to follow

- Jules Berman

key words: precision medicine, genetics, multi-step, pathogenesis, genetic heterogeneity, jules j berman Ph.D. M.D.

Infections Develop Via a Sequence of Biological Steps 14 Feb 2018 3:53 AM (7 years ago)

A prior post listed 7 assertions regarding the role of infectious organisms on the human genome. In the next few blogs we'll look at each assertion, in excerpts from Precision Medicine and the Reinvention of Human Disease. Here's the seventh:

By dissecting the biological steps involved in the pathogenesis of infectious disease, it is possible to develop new treatments, other than antibiotics, that will be effective against a range of related organisms.

Nature, by interfering with the different steps in the development of infectious diseases, has a variety of protective mechanisms against organisms. For example, to defend against malaria, nature has preserved various mutations that render red cells unsuitable hosts for malarial guests. For example, individuals with hemoglobin variants HbS (sickle cell trait), HbC, and HbE increase the likelihood that an infected red cell will lyse. Likewise, but for obscure reasons, regulatory defects in hemoglobin synthesis, as seen in thalassemia, may also confer some protection against malaria. Also, variations in a structural protein of erythrocytes, SLC4A1, causing ovalocytosis; and polymorphisms of the glucose-6-phosphate dehydrogenase gene [57] both seem to protect against malaria.

We see individuals resistant to malaria due to absence of the Duffy protein required for Plasmodium vivax to bind and enter erythrocytes [58]. Knowing this, the Duffy-binding protein in the malaria parasite is now being studied as a potential drug or vaccine target as a new strategy against malaria [58]. More generally, drugs known as entry inhibitors are being developed based on knowledge that the attachment and entry of organisms may depend upon specific cooperative pathways, in host and invader cells, that can be targeted by drugs. We know that there are many steps in the infection process that could be blocked by small changes in proteins that are unrelated to the immune process. For example, for an infectious agent to invade and flourish in an organism, it must gain entry into the tissues of the body, evading physical and chemical defenses along its way. It must find a place in which it can receive nourishment appropriate to its species and avoid any toxins that may be produced by its host. It must be able to grow as a collection of organisms, and this typically means that the host must permit some degree of invasion through its own tissues. These are just a few of the nonimmunological hurdles that invasive organisms must jump over, if they are to infect an organism. Every step in the pathogenesis of infectious disease provides another therapeutic opportunity. As we learn more about the pathways of development of infectious diseases that have become increasingly resistant to antibiotics, we will come to rely on Precision Medicine to prevent, diagnose, and treat infections.

- Jules Berman

key words: precision medicine, infections disease, biological steps, pathogenesis, jules j berman Ph.D., M.D.

Non-immunologic Causes of Increased Susceptibility to Disease 13 Feb 2018 3:26 AM (7 years ago)

A prior post listed 7 assertions regarding the role of infectious organisms on the human genome. In the next few blogs we'll look at each assertion, in excerpts from Precision Medicine and the Reinvention of Human Disease. Here's the sixth:

Cellular defects that have no direct connection to immunity may increase susceptibility to infectious organisms.

If we want to understand why certain individuals are susceptible to infections and other individuals are not, we must understand that immune deficiencies cannot account for all infections. Infectious diseases, just like any other disease, develop in steps, and it stands to reason that there must be many different pathways through which those steps can be enhanced or blocked. Theory aside, what is the actual evidence that susceptibility to infectious diseases arise through deficiencies unrelated to the immune system?

• Time and again, we encounter serious infections from organisms thought to be nonpathogenic, occurring in immunocompetent individuals [48–51].

• Not everyone with an immune deficit will succumb to an infectious disease, implying that these individuals are protected by resistance mechanisms other than immunity.

• We know of various genetic conditions that increase our susceptibility to infectious diseases, and some of these genetic flaws have nothing to do with the adaptive (i.e., antibody-forming) immune systems. For example, children with sickle cell disease or congenital asplenia will have a heightened susceptibility to invasive pneumococcal diseases [52]. Otherwise-normal children with IRAK4 or NEMO gene mutations will also have a high risk of invasive pneumococcal disease [52]. IRAK4 or NEMO genes code for proteins involved in the phagocytosis of bacteria by splenic macrophages. Likewise, in mice, natural resistance to infection is influenced by the Bcg gene, which affects the early phagocytosis and destruction of intracellular organisms by macrophages [53]. As a final example, both humans and zebrafish that have mutations that reduce the synthesis of a proinflammatory leukotriene have heightened susceptibility to Mycobacterium tuberculosis [54]. It is easy to find examples of nonimmunologic mechanisms for susceptibility to infections [55,56].

- Jules Berman

key words: precision medicine, immune system, susceptibility to disease, non-immunologic, jules j berman Ph.D., M.D.

Infection without Disease (from Precision Medicine and the Reinvention of Human Disease) 12 Feb 2018 2:34 AM (7 years ago)

A prior post listed 7 assertions regarding the role of infectious organisms on the human genome. In the next few blogs we'll look at each assertion, in excerpts from Precision Medicine and the Reinvention of Human Disease. Here's the fifth:

Normal defenses can block every infectious disease. Hence, every infectious disease results from a failure of our normal defenses, immunologic and otherwise.

For any given infectious agent, no matter how virulent they may seem, there are always individuals who can resist infection. Moreover, as a generalization, the majority of individuals who are infected with a pathogenic microorganism will never develop any clinically significant disease [42].

As one example, Naegleria fowleri is often found in warm freshwater. Swimmers in contaminated waters may develop an infection that spreads from the nasal sinuses to the central nervous system, to produce an encephalitis that is fatal in 97% of cases [43]. Despite the hazard posed by Naegleria, health authorities do not generally test freshwater sources to determine the presence of the organism. Do not expect to find warning signs posted at swimming holes announcing that the water is contaminated by an organism that produces a disease that has a nearly 100% fatality rate. It is simply assumed that anyone who spends any time around freshwater will eventually be exposed to Naegleria. As it happens, although many thousands of individuals are exposed each year to Naegleria in the United States, only a few cases of Naegleria encephalitis occur in this country. In fact, since Naegleria was recognized as a cause of encephalitis, in 1965, fewer than 150 cases have been reported [44]. Most of the reported cases have occurred in children and adolescents and are associated with recreational water activities [45,46]. The children who develop Naeglerian encephalitis, though exhibiting no signs of immune deficiency, are nonetheless susceptible to Naegleria. What makes these children different from all the other children and adults who were exposed to the same organisms?

Neisseria meningitidis, a cause of bacterial meningitis, can be cultured from nasal swabs sampled from the general population. If N. meningitidis were a primary pathogen, then why doesn’t it cause disease in the vast majority of infected individuals. If N. meningitidis were an opportunistic infection, then why does it typically cause disease in healthy college-age individuals (not immunocompromised individuals)? If this organism is neither a primary pathogen nor an opportunistic pathogen, then what kind of pathogen is it? More importantly, why is N. meningitidis a potentially fatal pathogen in some individuals and a harmless commensal in others [47]?

Organisms that were formerly thought to be purely pathogenic are now known to frequently live quietly within infected humans, without causing symptoms of disease. For example, parasites such as the agents that cause Chagas disease, leishmaniases, and toxoplasmosis are commonly found living in apparently normal individuals. Viruses, including the agents that cause herpes simplex infections and infections by hepatitis viruses B and C, can be found in healthy individuals. Mycobacterium tuberculosis can infect an individual, produce a limited pathologic reaction in the lung, and remain in the body in a quiescent state for the life of the individual. In fact, it has been estimated that about one out of three individuals, worldwide, is infected with Mycobacterium tuberculosis, and will never suffer any consequences. Luckily, asymptomatic carriers of tuberculosis, in whom the there is no active pulmonary disease, are noninfective. Staphylococcus aureus, a bacterial pathogen that is known to produce abscesses, invade through tissues, and release toxins, is also known to circulate in the blood, without causing symptoms, in a sizeable portion of the human population [40].

We now know that potentially virulent organisms are normally tamed within our bodies. Hence, the root cause of every clinical infection results from a deficiency in the defenses of particular subpopulations of individuals.

- Jules Berman

key words: precision medicine, commensals, symbiotes, symbiotic, host organisms, latent infection, jules j berman Ph.D. M.D.

Cellwise, We Are Mostly Inhuman 11 Feb 2018 3:05 AM (7 years ago)

A prior post listed 7 assertions regarding the role of infectious organisms on the human genome. In the next few blogs we'll look at each assertion, in excerpts from Precision Medicine and the Reinvention of Human Disease. Here's the fourth:

Most of the cells residing in human bodies are nonhuman

There are about 10 times as many nonhuman cells living in our bodies as there are human cells [40]. The human intestines alone contain 40,000 different species of bacteria [9]. These 40,000 species contain about 9 million different genes. Compare that with the paltry 23,000 genes in the human genome, and we quickly see that we homo sapiens contribute very little to the genetic diversity of the human body’s ecosystem.

- Jules Berman

key words: precision medicine, commensals, symbiotes, symbiotic, host organisms, jules j berman Ph.D. M.D.

Genome-Specific Responses to Infection 10 Feb 2018 2:53 AM (7 years ago)

A prior post listed 7 assertions regarding the role of infectious organisms on the human genome. In the next few blogs we'll look at each assertion, in excerpts from Precision Medicine and the Reinvention of Human Disease. Here's the third:

A good portion of the genes in humans (perhaps 10%) are involved in responses to infectious organisms.

It has been estimated that over 1000 human genes are involved in inflammation pathways [37]. Several studies have shown that following an inflammatory challenge or challenged by the introduction of a pathogen, more than a hundred genes are activated [38–40]. The activated genes include some of the same genes that have been associated with autoimmune diseases, suggesting that these disease-associated genes are conserved because they have a beneficial role, protecting us from invading pathogens [39]. The genetic profile of genes activated by inflammation is very similar from human to human, but quite dissimilar from the profile of genes activated by inflammation in the mouse [41]. This would suggest that species develop their own genome-wide responses to agents that cause inflammation (e.g., invading organisms).

- Jules Berman

key words: precision medicine, evolution, virus, viral, jules j berman Ph.D. M.D.

Vertebrate Evolution Driven by DNA from Infectious Organisms 9 Feb 2018 3:58 AM (7 years ago)

A prior post listed 7 assertions regarding the role of infectious organisms on the human genome. In the next few blogs we'll look at each assertion, in excerpts from Precision Medicine and the Reinvention of Human Disease. Here's the second:

Some of the key steps in the development of vertebrate animals, and mammals in particular, have come from DNA acquired from infectious organisms.

The human genome has preserved its viral ballast, at some cost. At every cell division, energy is expended to replicate the genome, and the larger the genome, the more energy must be expended. Why do we spend a large portion of the energy required to replicate our genome, on inactive sequences, of viral origin? Why doesn’t our genome simply eject the extra DNA, a biological process that is commonplace in the evolution of obligate intracellular parasitic organisms? Maybe it's because we use viral genes to our own advantage.

Two evolutionary leaps, benefiting the ancestral classes of humans, and owed to the acquisition of viral genes, include the attainment of adaptive immunity and the development of the mammalian placenta. Let’s take a moment to see how these innovations came about.

Adaptive immunity evolved at about the same time that jawed vertebrates first appeared on earth. The crucial gene responsible for the great leap to adaptive immunity, the recombination activating gene (RAG), was stolen from a retrovirus. To understand the pivotal evolutionary role of RAG, we need to review a bit of high school biology. The adaptive immune system responds to the specific chemical properties of foreign antigens, such as those that appear on viruses and other infectious agents. Adaptive immunity is a system wherein somatic T cells and B cells are produced, each with a unique and characteristic immunoglobulin (in the case of B cells) or T-cell receptor (in the case of T cells). Through a complex presentation and selection system, a foreign antigen elicits the replication of a B cell whose unique immunoglobulin molecule (i.e., so-called antibodies) matches the antigen. Secretion of matching antibodies leads to the production of antigen-antibody complexes that may deactivate and clear circulating antibodies, or may lead to the destruction of the organism that carries the antigen (e.g., virus or bacteria).

To produce the many unique B and T cells, each with a uniquely rearranged segment of DNA that encodes specific immunoglobulins or T-cell receptors, recombination and hypermutation must take place within a specific gene region. This process yields on the order of a billion unique somatic genes, and requires the participation of recombination activating genes (RAGs). The acquisition of a recombination activating gene is presumed to be the key evolutionary event that led to the development of the adaptive immune system present in all jawed vertebrates (gnathostomes). Before the appearance of the jawed vertebrates, this sort of recombination was genetically unavailable to animals. Our genes simply were not equal to the task. Retroviruses, however, are specialists at cutting, moving, and mutating DNA. Is it any wonder that the startling evolutionary leap to adaptive immunity was acquired from retrotransposons? Thus,we owe our most important defense against infections to genetic material retrieved from the vast trove of retrovirally derived DNA carried in our genome [33]. As one might expect, inherited mutations in RAG genes are the root causes of several immune deficiency syndromes [34,35].

Many millions of years later, vertebrates acquired another gene that did much to enable the evolution of all mammals. Members of Class Mammalia are distinguished by the development of the placenta, an organ that grows within the uterine cavity (i.e., the endometrium). After birth, the placenta must detach from the uterus. You can imagine the delicate balancing act between attaching firmly to the wall of the uterus and detaching cleanly from the wall of the uterus. During placental development, large, flat cells called cytotrophoblasts form the interface between placenta and uterus. To create the thin membrane that borders the lining of the uterus and that borders the blood received from the uterus in the spaces between the placental villi, the cytotrophoblasts must somehow fuse into a syncytium (i.e., multinucleate collections of cells that have fused together by dissolving their individual cytoplasmic membranes).

There is one task at which all animals excel: maintaining a clear separation between one cell and another. In point of fact, the most distinctive difference between animal cells and all other cells of eukaryotic origin happens to be the presence of cell junctions, whose purpose is to bind cells to one another without fusing cells. This being the case, you can see that the normal direction of animal evolution would preclude the appearance of a gene intended to form a huge syncytium of placental cells. Whereas animal cells are failures at fusion, viruses are champions. One of the most often-deployed methods by which viruses invade cells is through fusion at the cytoplasmic membrane. It happens that retroviral envelope genes, preserved in the human genome, do a very good job at fusing membranes. Animals captured a retroviral fusogenic envelope gene and inserted it into one of the first syncytin molecules involved the development of the placenta. Apparently, this acquisition worked out so well for mammals that later-evolving mammalian classes made their own retrovirus gene acquisitions to obtain additional syncytins, thus refining the placenta for their own subclasses [23,36].

- Jules Berman

key words: precision medicine, evolution, virus, viral, jules j berman Ph.D. M.D.

RELIC DNA IN THE HUMAN GENOME 8 Feb 2018 3:59 AM (7 years ago)

Yesterday's post listed 7 assertions regarding the role of infectious organisms on the human genome. In the next few blogs we'll look at each assertion, in excerpts from Precision Medicine and the Reinvention of Human Disease. Here's the first:

A significant portion of the human genome consists of relic DNA derived from ancient invasive organisms.

About 8% of our genome is derived from sequences with similarity to known infectious retroviruses, and these longer sequences can usually be recognized by their contained subsequences (e.g., gag, pol, and env genes) and long terminal repeats. The viral sequences in our genomes are the remnants of ancient retroviral infections, and the occasional nonretroviral infection, that were branded into DNA, and subsequently amplified [21–23]. Because much of the endogenous retroviral load in the human genome is due to amplification, and subsequent mutation, it is hard to determine the number of retroviral species that established their niche in the human gene pool, but studies of these viral remains would suggest that we contain species from several dozen families of retroviruses, with an undetermined number of contributions from individual family members [24]. Based on comparisons of the viruses present in different species of primates, it would appear that the most recent acquisition of an endogenous retrovirus occurred in humans between 100,000 and 1 million years ago [25]. Most of the retroviral sequences in our genomes are inactivated due to an accumulation of degenerative mutations collected over the eons, indicating that there has been little or no selective pressure to conserve retroviruses in their pristine sequences.

- Jules Berman

key words: precision medicine, human genome, evolution, infectious diseases, jules j berman, Ph.D., M.D.

Infections have made their mark on the Human Genome 7 Feb 2018 6:26 AM (7 years ago)

In the context of Precision Medicine, infections draw our attention because they have played an important role in the evolution of the eukaryotic genome. Over the next few blog posts, we will explore the following:

• A significant portion of the human genome consists of relic DNA derived from ancient invasive organisms.
• Some of the key steps in the development of vertebrate animals, and mammals in particular, have come from DNA acquired from infectious organisms.
• A good portion of the genes in humans (perhaps 10%) are involved in responses to infectious organisms.
• Most of the cells in the human (at least 90%) consist of infectious organisms and commensals that have adapted to life within human hosts. [Glossary Commensal]
• Normal defenses can block every infectious disease. Hence, every infectious disease results from a failure of our normal defenses, immunologic and otherwise.
• Cellular defects that have no direct connection to immunity may increase susceptibility to infectious organisms.
• By dissecting the biological steps involved in the pathogenesis of infectious disease, it is possible to develop new treatments, other than antibiotics, that will be effective against a range of related organisms.

Over the next few blogs, we'll do our best to justify each of these (as yet) unproven assertions.

- Jules Berman

key words: precision medicine, infections, evolution, resistance to infection, jules j berman Ph.D., M.D.

Precision Medicine and Public Health (from Precision Medicine and the Reinvention of Human Disease) 6 Feb 2018 3:37 AM (7 years ago)

Excerpted from Precision Medicine and the Reinvention of Human Disease

Despite having the most advanced healthcare technology on the planet, life expectancy in the United States is not particularly high. Citizens from most of the European countries and the highly industrialized Asian countries enjoy longer life expectancies than the United States. According to the World Health Organization, the United States ranks 31st among nations, trailing behind Greece, Chile, and Costa Rica, and barely edging out Cuba [42]. Similar rankings are reported by the US Central Intelligence Agency [43]. These findings lead us to infer that access to advanced technologies, such as those offered by Precision Medicine, will not extend lifespan significantly.

Every healthcare professional knows that most of the deaths occurring in this country can be attributed to personal lifestyle choices: smoking, drinking, drug abuse, and over-eating. Lifestyle diseases account for the majority of deaths in the United States and in otherwestern countries, these being:heartdisease,diabetes, obesity, andcancer.Population-basedtrials that seek to improve theways inwhichindividuals live, by introducing adaily exercise routine, healthydiet, and cigarette abstinence, have yielded huge benefits, in terms of extending average lifespans [44]. At the front end of the human life cycle, it has been demonstrated that infant mortalities can be markedly reduced with simple measures, focusing on improved maternal education [45]. It has been credibly argued that cleanwater, clean air, clean housing, clean food, and clean living yieldgreater societal benefits than clean operating rooms [46,47]. If this be the case, should we be investing heavily in Precision Medicine, when simple, low-tech public health measures are likely to provide a greater return on investment, in terms of overallmorbidity andmortality? In a certain sense, public health is the opposite of personalized medicine. Whereas personalized medicine involves finding the best possible treatment for individuals, based on their uniqueness, public health involves finding ways of treating whole populations based on their collective sameness. Let’s not dwell on these somewhat contrived philosophic points. Precision Medicine, as viewed in this book, is a new way of understanding human diseases. As such, Precision Medicine provides opportunities to advance both personalized medicine and public health.

Precision Medicine tells us that we should think of diseases as developmental process, with each step in the process representing an opportunity for intervention. Perhaps the most important function of Precision Medicine will be to give society the opportunity to institute public health measures aimed at blocking the pathogenesis of human diseases. Here are just a few examples:

– Population screening for early stages of common diseases.

The successful reduction in deaths from cervical cancer demonstrates the effectiveness of screening for early stages of disease. Cervical cancer is a type of squamous cell carcinoma that develops at the junction between the ectocervix (the squamous lined epithelium) and the endocervix (the glandular lined epithelium) in the os of the uterine cervix of women. Before the introduction of cervical precancer treatment, cervical carcinoma was one of the leading causes of cancer deaths in women worldwide. Today, in many countries that have not deployed precancer treatment, cervical cancer remains the leading cause of cancer deaths in women [48– 50]. In the United States, a 70% drop in cervical cancer deaths followed the adoption of routine Papsmear screening[51–53].Noeffort aimedat treatinginvasive cancers has providedanequivalent reduction in the number of cancer deaths. [Glossary Age-adjusted incidence, Pap smear] Today, we know that cervical carcinogenesis begins with a localized infection by one of several strains of human papillomavirus, transmitted during sexual intercourse by an infected male partner. In the late 1940s (and really up until the early 1980s), the viral etiology of cervical cancer was unknown. We did know that squamous cells sampled from the uterine os had highly characteristic morphologic appearances that preceded the development of invasive cancer. Thanks largely to the persistence of Dr. Papanicolaou and his coworkers, a standard screening test, known as the Pap smear, was developed to detect cervical precancers. If precancerous changes were found in a smear, a gynecologist could remove a superficial portion of the affected epithelium, and this would, in the vast majority of cases, stop the cancer from ever developing.

Morphologic and epidemiologic observations on Pap smears provided clues that eventually led to the identification of several strains of human papillomavirus as the major causes of cervical cancer. Today, a vaccine protective against carcinogenic strains of human papilloma virus is available [54].

As discussed in Precision Medicine and the Reinvention of Human Disease, Section 7.5, “What Is Precision Diagnosis?” new biomarkers are being developed for the early stages of disease, often preceding the development of any clinical symptoms. In general, diseases are easiest to treat in early stages, before they have had the chance to do any harm to organs. For example, precancers can often be effectively treated by excision, or, in some cases, by withdrawal of the agents that would otherwise lead to the progression of the precancer to the cancerous stage (e.g., cessation of hormonal replacement therapy to block breast cancer, cessation of smoking to block lung cancer, treatment of Helicobacter pylori infection to block MALToma).

We can hope that in the future advances in the field of Precision Medicine will identify the intermediate stages of development for common diseases. With this information, public health measures aimed at detecting and blocking diseases, in an early stage of development, will be deployed.

– The aggressive prevention and treatment for the most common patterns of diseases that lead to death

As discussed in Precision Medicine and the Reinvention of Human Disease, Section 2.3, “Cause of Death,” a well-composed death certificate contains a thoughtful sequence of medical conditions that develop over time, and that ultimately lead to the death of the patient. This data, if properly recorded and aggregated into a mortality database, should provide the most frequently occurring chains of events that account for human deaths. A public health effort aimed at breaking the early steps of these processes has the potential of extending the life expectancy of the population.

– Aggressive screening for carriers of infectious diseases

As discussed in Section 6.2, “Our Genome Is a Book Titled ‘The History of Human Infections,’” organisms that were formerly thought to be purely pathogenic are now known to frequently live quietly within infected humans, without causing symptoms of disease, and this would include the organisms that cause Chagas disease, leishmaniases, toxoplasmosis, tuberculosis, viruses such as Herpes viruses and hepatitis viruses B and C, and bacterial organisms, some of which circulate in the blood without causing disease under normal circumstances.

Sensitive diagnostic techniques, including genome sequencing of DNA in blood, may provide us with the opportunity to perform population screening for organisms that are opportunistic pathogens, or that produce long-term damage to carriers, or that are transmissible from carriers.

– Finding targets for vaccines that confer effectiveness against more than one target organism.

Thanks in no small part to Precision Medicine, we are learning that organisms play a role in many diseases that were once thought to have no infectious component. In particular, it is now widely accepted that infections contribute to at least one-fifth of all cancers occurring in humans. Examples of cancer causing organisms are:

– Epstein-Barr virus (B-cell lymphomas, Burkitt lymphoma, 
nasopharyngeal cancer, Hodgkin disease and T-cell lymphomas)
– Hepatitis B virus (hepatocellular carcinoma)
– Human papillomavirus types 5, 8, 14, 17, 20, 
and 47 (skin cancer)
– Human papillomavirus types 16, 18, 31, 33, 35, 39, 
45, 52, 56, 58 (cervical cancer, anogenital cancer)
– Human papillomavirus types 6 and 11 (verrucous 
carcinoma)
– Human papillomavirus types 16, 18, 33, 57, 73 
(cancers of oral cavity, tongue, larynx, nasal cavity, 
and esophagus)
– Merkel cell polyomavirus (MCPyV) (Merkel cell carcinoma)
– HTLV-1 (adult T-cell leukemia)
– Human herpesvirus 8 (Kaposi sarcoma)
– Hepatitis C virus—hepatocellular carcinoma 
and low-grade lymphomas
– JC, BK, and SV40-like polyoma viruses (tumors 
of brain and pancreatic islet tumors, and mesotheliomas)
– Human endogenous retrovirus HERV-K 
(seminomas and germ cell tumors)
– Schistosomiasis and squamous cell carcinoma of 
bladder
– Opisthorchis viverrini and Clinorchis sinensis, 
flatworms (flukes), found in Southeast Asia, 
(cholangiocarcinoma)
– Helicobacter pylori and gastric MALToma 
(Mucosa-Associated Lympoid tissue
lymphoma) [55]

Carcinogenic viruses profoundly influence the number of cancer deaths, worldwide. These include hepatitis B virus (associated with an increased incidence of hepatocellular carcinoma) and human papillomavirus (which causes cervical cancer). Liver cancer is the third leading cause of cancer deaths worldwide, accounting for 611,000 deaths in 2000 [50]. It is easy to understand that the importance of vaccine development for infections that contribute to chronic diseases and cancers cannot be overstated. As we learn more about the biological steps involved in the infection process, hope looms that vaccines and preventive drugs will be developed that target different types of organisms, based on shared properties of infection, invasion, immunologic resistance, persistence, or phylogeny, as discussed in Precision Medicine and the Reinvention of Human Disease, Section 4.4, “Pathway-Directed Treatments for Convergent Diseases,” [56–60].

- Jules Berman

key words: public health, prevention, precision medicine, cancer, cancer vaccines, jules j berman, Ph.D., M.D.

Treat the Pathway, not the Gene (from Precision Medicine and the Reinvention of Human Disease) 5 Feb 2018 2:37 AM (7 years ago)

Treat the key pathway, not the genetic mutation (from Precision Medicine and the Reinvention of Human Disease)

Some of the earliest and most successful Precision Medication drugs have targeted specific mutations occurring in specific subsets of diseases. One such example is ivacaftor, which targets the G551D mutation present in about 4% of individuals with cystic fibrosis [135]. It is seldom wise to argue with success, but it must be mentioned that the cost of developing a new drug is about $5 billion [136]. To provide some perspective, $5 billion exceeds the total gross national product of many countries, including Sierra Leone, Swaziland, Suriname, Guyana, Liberia, and the Central African Republic. Many factors contribute to the development costs, but the most significant is the incredibly high failure rate of candidate drugs. About 95% of the experimental medicines that are studied in humans fail to be both effective and safe. The costs of drug development are reflected in the rising costs of drugs.

When a new drug is marketed to a very small population of affected individuals, the cost of treating an individual may be astronomical. Americans should not pin their hopes on the belief that one day, the FDA or CMS (which administrates Medicare) will step in and put a stop to the price rises. The Food and Drug Administration can approve or reject drugs, but it does not regulate prices. Likewise, Medicare is not permitted to consider cost when it decides whether a treatment can be covered. Knowing this, some notable pharmaceutical companies have raised the prices of medications far beyond their manufacturing costs [137–139]. In effect, the cost of curing curable diseases may exceed our ability to pay for those cures [139].

It is strongly in the interests of society to develop drugs that have the widest possible user market [140]. Drugs that target a mutation that is specific for a few individuals with a rare disease, or a tiny subpopulation of individuals who have a common disease, are highly problematic.

Our experiences with disease convergence teach us that clinical phenotypes are influenced by the activities of pathways and are seldom restricted to a specific mutation in a specific gene. We know this because rare diseases that exhibit locus heterogeneity affect different genes, but often target the same pathway. Likewise, acquired phenotypes of genetic diseases often involve inhibitors of the same key pathways that drive their genetic counterparts, without involving the protein product of the genetic form of the disease. We also know that the acquired version of most genetic diseases account for the bulk of disease occurrences. Therefore, if we want to develop treatments that benefit the greatest number of individuals affected by a disease, it would be far more practical to find treatments that target the disease-driving pathways than to design drugs that target a specific gene mutation involved in a small subset of affected patients.

Before closing, here are a few points worth considering (to be discussed in later blogs):

• As a generalization, any drug that can block a pathway, without producing serious side effects, may serve as a candidate treatment for all of the diseases that are driven by the pathway.

• Individuals in the early stages of common diseases, before multiple disease pathways converge to produce an intractable clinical phenotype, may be particularly amenable to treatments that interfere with the pathways that promote the ensuing steps in pathogenesis.

The topic of clinical trials designed to test drugs targeting convergent disease pathways is discussed in Precision Medicine and the Reinvention of Human Disease, Section 9.6, “Fast, Cheap, Precise Clinical Trials.”

- Jules Berman

key words: precision medicine, precision treatment, clinical trials, cost of precision medicine, pathways, convergent pathways, jules j berman Ph.D., M.D.

National Patient Identifiers (from Precision Medicine and the Reinvention of Human Disease) 4 Feb 2018 4:46 AM (7 years ago)

Readers from outside the United States are probably wondering why the United States agonizes over the problem of patient identification. In many other countries, individuals are given a unique national identifier, and all medical data associated with the individual is kept in a central data repository under the aegis of the government’s health service. A single, permanent identifier is used by a patient throughout life, in every encounter with a hospital, clinic, or private physician. As a resource for researchers, the national patient identifier ensures the completeness of data sets and eliminates many of the problems associated with poorly implemented local identifier systems.

In the United States, there has been fierce resistance to the idea of national patient identifiers. The call for a national patient identification system is raised from time to time. The benefits to patients and to society are many. Regardless, US citizens are reluctant to have an identifying number that is associated with a federally controlled electronic record of their private medical information. In part, this distrust results from the lack of any national insurance system in the United States. Most health insurance in the United States is private, and private insurers have wide discretion over the fees and services provided to enrollees. There is a fear that if there were a national patient identifier with centralized electronic medical records, insurers would withhold reimbursements or raise premiums or otherwise endanger the health of patients. Because the cost of US medical care is the highest in the world, medical bills for uninsured patients can quickly mount, impoverishing individuals and families.

Realistically, though, no data is safe. Medical records can be stolen, and governments can demand access to medical records, when necessary [See Lewin T. Texas orders health clinics to turn over patient data. The New York Times; October 23, 2015].

Life has its compromises. Everyone wants their privacy and we all get angry when we hear that our confidential information has been stolen. Data breaches today may involve hundreds of millions of confidential records. The majority of Americans have had social security numbers, credit card information, and private identifiers (e.g., birth dates, city of birth, names of relatives) misappropriated or stolen. It’s natural to object to anything that might jeopardize our privacy. Nonetheless, we must ask ourselves the following: “Is it rational to forfeit the very real opportunity of developing new safe and effective treatments for serious diseases, for the very small likelihood that someone will crack your deidentified research record and somehow leverage this information to your disadvantage?”

Suppose everyone in the United States were given a choice: you can be included in a national patient identifier system, or you can opt out. Most likely, there would be many millions of citizens who would opt out of the offer, seeing no particular advantage in having a national patient identifier, and sensing some potential harm. Now, suppose you were told that if you chose to opt out, you would not be permitted to use any of the therapeutic or preventive benefits that come from studies performed with data collected from the national patient identifier system. These lost benefits would include safe and effective drugs, warnings of emerging epidemics, information on side effects associated with your medications, biomarker tests for preventable illnesses, and so on. Those who made no effort to help the system would be barred from any of the benefits that the system provided. Would you reconsider your refusal to cooperate, if you knew the consequences? Of course, this is a fanciful scenario, but it makes a point.

- Jules Berman

key words: identification, confidentiality, privacy, medical identifier, NPI, national patient identifier, jules j berman, Ph.D., M.D.

Paradoxes of Classification (and terrible Class definitions) 3 Feb 2018 8:05 AM (7 years ago)

The formal systems that assign data objects to classes, and that relate classes to other classes, are known as ontologies. When the data within a Big Data resource is classified within an ontology, data analysts can determine whether observations on a single object will apply to other objects in the same class. Similarly, data analysts can begin to ask whether observations that hold true for a class of objects will relate to other classes of objects. Basically, ontologies help scientists fulfill one of their most important tasks; determining how things relate to other things.

A classification is a very simple form of ontology, in which each class is allowed to have only one parent class. To build a classification, the ontologist must do the following: 1) define classes (i.e., find the properties that define a class and extend to the subclasses of the class); 2) assign instances to classes; 3) position classes within the hierarchy; and 4) test and validate all the above.

The constructed classification becomes a hierarchy of data objects conforming to a set of principles:

• The classes (groups with members) of the hierarchy have a set of properties or rules that extend to every member of the class and to all of the subclasses of the class, to the exclusion of unrelated classes . A subclass is itself a type of class wherein the members have the defining class properties of the parent class plus some additional property(ies) specific for the subclass.

• In a hierarchical classification, each subclass may have no more than one parent class. The root (top) class has no parent class. The biological classification of living organisms is a hierarchical classification.
• At the bottom of the hierarchy is the class instance. For example, your copy of this book is an instance of the class of objects known as "books".
• Every instance belongs to exactly one class.
• Instances and classes do not change their positions in the classification. As examples, a horse never transforms into a sheep, and a book never transforms into a harpsichord.
• The members of classes may be highly similar to one another, but their similarities result from their membership in the same class (i.e., conforming to class properties), and not the other way around (i.e., similarity alone cannot define class inclusion).

Classifications are always simple; the parental classes of any instance of the classification can be traced as a simple, non-branched list, ascending through the class hierarchy. As an example, here is the lineage for the domestic horse (Equus caballus), from the classification of living organisms:

Equus caballus
Equus subg. Equus
Equus
Equidae
Perissodactyla
Laurasiatheria
Eutheria
Theria
Mammalia
Amniota
Tetrapoda
Sarcopterygii
Euteleostomi
Teleostomi
Gnathostomata
Vertebrata
Craniata
Chordata
Deuterostomia
Coelomata
Bilateria
Eumetazoa
Metazoa
Fungi/Metazoa group
Eukaryota
cellular organisms

Taxonomists who view this lineage instantly grasp the place of domestic horses in the classification of all living organisms.

The rules for constructing classifications seem obvious and simplistic. Surprisingly, the task of building a logical, and self-consistent classification is extremely difficult. Most classifications are rife with logical inconsistencies and paradoxes. Let's look at a few examples.

In 1975, while touring the Bethesda, Maryland campus of the National Institutes of Health, I was informed that their Building 10, was the largest all-brick building in the world, providing a home to over 7 million bricks . Soon thereafter, an ambitious construction project was undertaken to greatly expand the size of Building 10. When the work was finished, building 10 was no longer the largest all-brick building in the world. What happened? The builders used material other than brick, and Building 10 lost its classification as an all-brick building, violating the immutability rule of class assignments.

Apparent paradoxes that plague any formal conceptualization of classifications are not difficult to find. Let's look at a few more examples.

Consider the geometric class of ellipses; planar objects in which the sum of the distances to two focal points is constant. Class Circle is a child of Class Ellipse, for which the two focal points of instance members occupy the same position, in the center, producing a radius of constant size. Imagine that Class Ellipse is provided with a class method called "stretch", in which the foci are moved further apart, thus producing flatter objects. When the parent class "stretch" method is applied to members of the Class Circle, the circle stops being a circle and becomes an ordinary ellipse. Hence the inherited "stretch" method forces members of Class Circle to transition out of their assigned class, violating the intransitive rule of classifications.

Let's look at the "Bag" class of objects. A "Bag" is a collection of objects, and the Class Bag is included in most object-oriented programming languages. A "Set" is also a collection of objects (i.e., a subclass of Bag), with the special feature that duplicate instances are not permitted. For example, if Kansas is a member of the set of U.S. States, then you cannot add a second state named "Kansas" to the set. If Class Bag were to have an "increment" method, that added "1" to the total count of objects in the bag, whenever an object is added to Class Bag, then the "increment" method would be inherited by all of the subclasses of Class Bag, including Class Set. But Class Set cannot increase in size when duplicate items are added. Hence, inheritance creates a paradox in the Class Set.

How does a data scientist deal with class objects that disappear from their assigned class and reappear elsewhere? In the examples discussed here, we saw the following:

1. Building 10 at NIH was defined as the largest all-brick building in the world. Strictly speaking, Building 10 was a structure, and it had a certain weight and dimensions, and it was constructed of brick. "Brick" is an attribute or property of buildings, and properties cannot form the basis of a class of building, if they are not a constant feature shared by all members of the class (i.e., some buildings have bricks; others do not). Had we not conceptualized an "all-brick" class of building, we would have avoided any confusion.

2. Class Circle qualified as a member of Class Ellipse, because a circle can be imagined as an ellipse whose two focal points happen to occupy the same location. Had we defined Class Ellipse to specify that class members must have two separate focal points, we could have excluded circles from class Ellipse. Hence, we could have safely included the stretch method in Class Ellipse without creating a paradox.

3. Class Set was made a subset of Class Bag, but the increment method of class Bag could not apply to Class Set. We created Class Set without taking into account the basic properties of Class Bag, which must apply to all its subclasses. Perhaps it would have been better if Class Set and Class Bag were created as children of Class Collection; each with its own set of properties.

Worst Class Definition Ever

The worst definition of a Class may have been that given to the Kingdom of Protozoa, defined as the class of one-celled eukaryotic organisms. The problem here is that all of the classes of multicelled organisms (e.g., animals, plants and fungi) descended from classes of one-celled organisms. This means that Class Protozoa (defined as one-cell organisms) must exclude from its lineage all descendant classes that are multicellular. Hence, Kingdom Protozoa was given a definition that, paradoxically, excluded its own descendants. What there they thinking, back in the mid-19th century when Class Protozoa was conceived?

- Jules Berman

key words: classification, ontology, taxonomy, paradoxes, precision medicine, jules j berman Ph.D., M.D.

Precision Medicine and the Reinvention of Human Disease (not just about genes) 1 Feb 2018 3:17 AM (7 years ago)

If everything you know about Precision Medicine comes from the lay press, you may have an unrealistic notion of what's happening in this field. The news seems to stress the one gene -> one disease paradigm that is easy to understand, but largely irrelevant to all the common diseases that occur in humans.

The one gene -> one disease paradigm is this: the clinical expression of each disease is caused by a genetic mutation in a particular gene responsible for that particular disease, or a particular subtype of a disease, in a particular individual. By finding and targeting the gene responsible for an individual's disease, Precision Medicine will cure the patient.

This paradigm is short and sweet, and it is more or less true for a number of rare diseases; but it is wrong for just about every disease that occurs commonly in humans, and it serves to distract our attention from the medical revolution that Precision Medicine will bring.

The purpose of my new book, Precision Medicine and the Reinvention of Human Disease, discussed in previous blogs, is to explain how Precision Medicine is changing our fundamental understanding of the pathogenesis of disease (i.e., the biological steps that lead to the development of diseases), and how this new information is changing the way that we prevent, diagnose, and treat human diseases.

Precision Medicine is not about finding the right gene for the right patient. Precision Medicine is about finding the common events and metabolic pathways that account for the development and the expression of diseases; and using these insights to reduce the morbidity and mortality of disease in the population.

Google Books has a very good "look inside" for my book, and I hope that readers of this blog will take a few moments to see if they might be interested in the subject.

- Jules Berman

key words: precision medicine, jules j berman, Ph.D., M.D., disease biology, pathogenetic, monogenic, rare diseases, complex diseases, common diseases

Precision Medicine and the Reinvention of Human Disease (a better definition) 28 Jan 2018 2:50 PM (7 years ago)

We can define Precision Medicine as an approach to the prevention, diagnosis, and treatment of disease that is based on a deep understanding of the sequence of biological events that lead to disease. With this approach we are learning:

(1) that we can develop new drugs that target specific steps in the development of disease;

(2) that drugs developed to interfere with a cellular event or pathway may serve as effective treatments for those individuals whose disease is driven by the pathway; and

(3) that a treatment effective for a subtype of one disease may also be effective against other diseases that happen to be driven by the same pathway.

This approach, based on learning the steps that precede the development of disease, shifts the emphasis of Precision Medicine from finding unique treatments for unique individuals to finding general treatments that are effective against precisely identified biological processes, in whichever diseases those processes may occur. In the era of Precision Medicine, every disease has a biological history, and every event in the history of the disease is a possible target for prevention, diagnosis, or treatment. At this point, we can begin to see the thread of a story that will unfold throughout my book, Precision Medicine and the Reinvention of Human Disease.

-Jules Berman

key words: precision medicine, definition, pathogenesis, steps in disease development, jules j berman

Precision Medicine and the Reinvention of Human Disease (not just the genome) 27 Jan 2018 4:29 AM (7 years ago)

If you believe the lay press, Precision Medicine involves sequencing a patient's genome and determining the proper treatment based on the individual's unique genetic attributes. The NIH (National Institutes of Health) seems to be encouraging this interpretation of the field. From the US National Institutes of Health comes the following description: "Precision Medicine is an emerging approach for disease prevention and treatment that takes into account people's individual variations in genes, environment, and lifestyle. The Precision Medicine Initiative will generate the scientific evidence needed to move the concept of Precision Medicine into clinical practice". An Advisory Committee to the NIH Director would include, under the mantle of Precision Medicine, "providing individual side-effect profiles of drugs, and preventative health care check-ups that include specific recommendations developed from interpreting an individual's genetic risk profile".

Between the millions of inter-individual variations in our genomes, the highly personalized lifestyle choices, and the differences in our environments, there seems to be plenty of uniqueness to spread around. It is easy to forget that our uniqueness as individuals often has much less to do with our diseases than does our sameness as members of the same species. Our sameness goes a long way toward explaining why humans seem to suffer from the same list of textbook diseases, regardless of their individualized genes and geography. Someone had to put the brakes on this epidemic of uniqueness.

Much to their credit, the National Research Council of the US National Academies tacked on the following caveat to the definition of Precision Medicine: "It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease, in the biology and/or prognosis of those diseases they may develop, or in their response to a specific treatment". The Research Council wisely distinguished Personalized Medicine from Precision Medicine, by adding, "Although the term 'Personalized Medicine' is also used to convey this meaning, that term is sometimes misinterpreted as implying that unique treatments can be designed for each individual. For this reason, the Committee thinks that the term 'Precision Medicine' is preferable to 'Personalized Medicine'"

The National Research Council pointed out what should have been obvious from the start. We cannot provide individualized treatments, because treatments must be tested for safety and efficacy on groups of people. The best we can ever do is to assign patients to a group that has been fitted to a preapproved treatment. So where does this leave us?

More to follow in tomorrow's blog.

- Jules Berman

key words: precision medicine, genomics, individualized treatments, definition, jules j berman, Jules Berman Ph.D., M.D.

Precision Medicine and the Reinvention of Human Disease (from Preface) 26 Jan 2018 3:14 AM (7 years ago)

Something has happened in the past two decades that has changed the way that modern biomedical scientist thinks about diseases. Because the changes in our perceptions have happened slowly, few of us have really taken notice of what it all means. The purpose of my latest book, Precision Medicine and the Reinvention of Human Disease, published January, 2018, is to show how advances in the field of Precision Medicine will forever change the way we understand and treat disease. Specifically, these advances are:

• Diseases develop in steps. Modern methodology has enabled us to dissect the biological events and metabolic pathways that ultimately lead to the expression of disease. We can no longer think in terms of the "cause" of a disease, because most diseases have multiple contributory causes, that act over time. [Glossary Pathway]

• Because disease development requires the successful completion of multiple, sequential steps, and because we can now observe some of these steps, Precision Medicine has given us multiple targets that we can attack, with the expectation of preventing diseases from developing, delaying the development of disease, or treating diseases that are driven by identifiable pathways.

• Because different paths of development may lead to the same set of clinical findings, diseases can be subtyped into classes according to the specific pathways that drive their biology. Hence, treatment can be precisely targeted to subtypes of diseases that were formerly indistinguishable from one another.

• Because diseases that appear to be unrelated might share biological pathways that can be successfully targeted by new classes of drugs, we can now prevent or treat a variety of diseases, using a drug that was specifically developed for one rare subtype of disease.

Precision Medicine and the Reinvention of Human Disease explains how we have come to believe that these four advances in Precision Medicine are true, and how these advances are impacting the practice of medicine.

- Jules Berman

key words: precision medicine, rare diseases, jules j berman, jules berman, pathogenesis

Precision Medicine and the Reinvention of Human Disease (The Myth) 25 Jan 2018 2:55 AM (7 years ago)

If you believe the hype, we are entering a new era of medicine in which each individual will receive unique treatment, determined by the sequence of his or her genome. This widely promulgated notion is simply ridiculous. There is no practical way to develop a unique treatment, test the treatment for safety and effectiveness, and titrate the correct dose, all for one person.

The terms "Precision Medicine" and "Personalized Medicine" have given us the false impression that medical science is moving away from off-the-rack remedies and is seeking treatments tailored to the individual. In actuality, science has always been about seeking generalization. When Isaac Newton watched an apple drop, he was not working on a new Law of Falling Apples. He was trying to understand the general laws of gravity and motion that applied to every object in the universe. When Charles Darwin spent 8 years studying barnacles, he was not trying to build a display collection of handsome barnacles for the national museum. He was developing a general theory of evolution that would apply to every living organism on earth. Likewise, when we study a specific pathway that is operative in a small percentage of cases of a rare tumor, accounting for perhaps a dozen patients worldwide, we expect that our findings will have general application to a wide variety of conditions.

Precision Medicine has very little to do with developing unique treatments. Like all medical research, Precision Medicine seeks to find general treatments that will be effective for the largest number of patients. The "Precision" in Precision Medicine refers to our ability to precisely diagnose rare diseases, and subsets of common diseases, that share a particular sensitivity to particular forms of treatment.

- Jules Berman

Precision Medicine and the Reinvention of Human Disease (Book Index) 23 Jan 2018 3:31 AM (7 years ago)

In January, 2018, Academic Press published my book Precision Medicine and the Reinvention of Human Disease. This book has an excellent "look inside" at its Google book site, which includes the Table of Contents. In addition, I thought it might be helpful to see the topics listed in the Book's index. Note that page numbers followed by f indicate figures, t indicate tables, and ge indicate glossary terms.

A
Abandonware, 270, 310ge
Ab initio, 34, 48ge, 108ge
ABL (abelson leukemia) gene, 28, 58ge, 95–97
Absidia corymbifera, 218
Acanthameoba, 213
Acanthosis nigricans, 144ge
Achondroplasia, 74, 143ge, 354ge
Acne, 54ge, 198, 220ge
Acquired autoantibody disease, 133
Acquired Parkinsonism, 105ge, 128, 281
Acrodermatisis verruciformis, 26–27
Acrokeratosis verruciformis of Hopf, 23–24
Actinic keratoses, 33
Actinobacillus actinomycetemcomitans, 217
Actinomyces pyogenes, 217
Activated oncogene, 28, 34–35, 57ge, 222ge
Acute anterior uveitis, 208
Acute flaccid myelitis, 204–205
Acutely transforming retroviruses, 46–47
Acute myelogenous/myeloid leukemia, 33–34, 97, 139,
156–157
Adaptive immunity evolved, 192
Adenosine deaminase, 46, 72, 80
Adrenocortical carcinoma, 73, 103ge
Adult T-cell leukemia, 339
Adverse effect, 275, 352–353
Aflatoxin, 30, 182
  and hepatocellular carcinoma, 30, 182
Age adjusted incidence, 338, 354ge
Age related macular degeneration, 105ge, 138, 199
Aggregate disease, 80, 98ge
Aging, 35–36, 70–71, 125–126, 234, 354
Agouti, 86–87
AIRE gene, 198
Albinism, 77–78, 81–82, 84
Alcohol, 128, 156, 159, 287
  abuse, 128, 158
  related neurodevelopmental disorders, 159
Allele, 70, 98ge, 158–159, 332–333, 347–348
Allelic heterogeneity, 71, 98ge
Alpha1 antitrypsin deficiency, 82
Alpha particles, 166–167
Alpha-spectrin gene, 70
Alpha thalassemia, 83, 92
Alstrom syndrome, 122–123
Alternative RNA, 85, 98ge
Alzheimer disease, 6–7, 70, 139, 233–234, 246, 327–328, 353
Amateur scientists, 329–330
Aminoglycoside, 128
  induced hearing loss, 77
Amoebic encephalitides, 213
Amphotericin B, 128, 213
Amyloidosis, 83, 98ge, 200
Amyloid plaques, 126
Amyotrophic lateral sclerosis, 77, 282
Anaerobic conditions, 108ge
Anal squamous carcinoma, 45
Anaphylactic shock, 133
Anaphylaxis, 133
Anaplasma phagocytophilum, 217
Anatomic abnormalities, 6–7, 166–167
Ancestral class, 184, 231
Aneuploid, 89, 89f
Aneuploid cells, 49ge
Aneuploidy, 47, 89f
Angelman syndrome, 75, 86
Angiofollicular lymph node hyperplasia, 50ge
Angiogenesis, 120, 138
Angioimmunoblastic lymphadenopathy, 55ge
Angiopoietin, 139
Angiotensin converting enzyme inhibitors, 4
Animal model, 31, 33, 88, 170, 279, 351–354
Ankylosing spondylitis, 208
Anlagen, 243
Annotation, 264, 271, 308, 341
Anonymizing private and confidential medical data, 310
Anoxia, 91–92
Anterior segment mesenchymal dysgenesis, 53ge
Anthers, 90
Antibasement membrane antigen, 132
Antibiotics, 190, 195–196, 210, 213
Antibody/antibodies, 21–22, 73, 126, 130–134, 139, 192,
197, 209, 299, 348–350
Anticipation, 86
Anucleate ovum, 254ge
Anucleate red blood cell, 91
Aortic dissection, 142ge
Apicomplexans, 184, 186
Aplastic anemia, 24–25, 95–96, 166
Apolipoprotein E (APOE) gene, 70
Apoptosis, 48–61, 73, 133–134, 197–200
Archaeans, 191
Archaeplastida, 186, 191–192
Aromatase inhibitors, 136–137
Arrhythmias, 135
Arrhythmogenic right ventricular cardiomyopathy, 331
Arrhythmogenic right ventricular dysplasia, 70
Artemis gene, 72
Arteriosclerosis, 171
Arthritis, 131–132, 134, 139, 197–198, 201, 208, 346,
349–350
Arthropod vectors, 202
Arylsulfatase, 121–122
  deficiency, 121–122
Asbestos, 26–27, 29, 167–169, 279–280
Ash leaf spots, 102ge
Aspergillus flavus, 182
Aspergillus fumigatus, 214
Aspirin, 124, 135
Asplenia/polysplenia, 72
Ataxia, 73, 83, 91, 128, 159
telangiectasia, 91
Atherosclerosis, 158, 171, 199
Atrial myxomas, 95–96
Atrioventricular conduction defects, 53ge
ATRX gene, 92
Atypical hemolytic uremic syndrome, 199
Atypical intraductal hyperplasia, 32
Authentication, 273
Autism, 7, 138
Autoantibodies, 133–135, 137–138
Autoantibody disease, 130–134, 197
Autocoders, 283
Autocoding, 277, 282–283
Autocorrelation, 267
Autoimmune disease, 21–22, 131–134, 193, 197–198, 251
Autoimmune disorder characterized, 131
Autoimmune hemolytic anemia, 144ge
Autoimmune lymphoproliferative syndrome (ALPS),
133–134, 197–198
Autoimmune polyendocrinopathy-candidiasisectodermal
dystrophy, 198
Autoimmune thyroiditis, 201
Autoimmune variant, 73, 123–124
Autoinflammatory diseases, 197, 235
Autonomous growth, 46
Autophagy, 83
Autopsies, 81
Autopsy report, 36, 277
Autosomal dominant hearing loss, 104ge, 106ge
Autosomal dominant polycystic kidney disease, 122–123
Autumn crocus (Colchicum autumnale), 200
Axonal neuropathy, 70
Azacytidine, 87

B
Bacillary angiomatosis, 202, 210–211
Bacille Calmette-Guerin (BCG), 22–23
Bacillus
  B. globigii, 212
  B. hoagii, 218
Bacteremia, 202, 213
Bacteroides
  B. fragilis, 214
  B. vulgatus, 214
Bannayan-Riley-Ruvalcaba syndrome, 156, 173ge
Bardet-Biedl syndrome, 72, 122–123, 157
Barr body, 85–86
Bartonella
  B. henhenselae, 210–211
  B. quintana, 202, 217
Bartonella species, 202–203
Bartter syndrome, 136f
Basal body, 72
Basal cell carcinoma, 158, 170
Base pairs (bp), 84, 86
Bastocystis, 213
B cell, 161, 192, 339, 346, 349–350  
  acute lymphoblastic leukemia, 160–161
  lymphomas, 339, 349–350
BCR-ABL, 56ge
Bcr/abl fusion gene, 28, 95–97, 251
BCR (breakpoint cluster region) gene, 58ge
Beckwith-Wiedemann syndrome, 75, 86, 106ge
Behcet disease, 99ge, 206
Benign tumor, 26–27, 50ge, 176ge
Benzene, 166, 169
Bernard-Soulier syndrome, 59ge, 135
Bevacizumab, 138, 142ge
Bilateral acoustic schwannomas, 56ge
Bilateral progressive sensorineural hearing loss, 135
Bilateral retinoblastomas, 318ge
Biliary atresia, 122–123
Bimodal age distribution, 52ge, 104ge, 292–293
Biomarker development, 252
Birth defects, 159, 335
Bisphossy jaw, 168–169
Bite infections, 213
BK virus, 339
Bladder catheters, 214
Bladder tumor, 95
Blast cells, 104ge
Blastocystis, 213
Blast transformation, 104ge
Blastula, 3, 51ge, 85, 235, 255ge, 335–336, 356ge
Blau syndrome, 54ge, 199, 220ge
Blended class, 205–206, 218ge, 246, 252ge, 258ge
Blepharspasm, 350
Blistering disease, 123–124, 132
Blood cell lineages, 49ge
Blood cells, 28, 82, 91, 131, 133–134, 137, 139–140,
164–166, 172, 197–198, 281, 295, 328
Blood clotting cascade, 164
Bloom syndrome, 50ge
Boder sedgwick syndrome, 99ge
Bone marrow failure, 24–25
Bone marrow necrosis, 30
Bone marrow stem cells, 49ge
Bone marrow toxins, 166
Bootstrapping, 232, 287–288
Botox, 138, 171, 350
Bovine spongiform encephalitis, 219ge
Bovine spongiform encephalopathy, 144ge
BRAF, 290, 347–348
  mutation, 347–348
  V600E, 94–95
Brain tubers, 354
Branchiootorenal syndrome, 53ge
BRCA gene, 28, 119, 126
Breakpoint cluster region (BCR) gene, 58ge
Breast cancer, 32, 32f, 73–74, 95, 119, 126, 136–137, 250,
252, 299, 338, 347–348, 352
Bronchiectasis, 123, 289
Bronchioloalveolar carcinoma, 173ge
Bronchoalveolar carcinoma, 173ge
Bronchogenic carcinoma, 169
Bronchogenic lung cancer, 159, 167
Brucellosis, 200
Bruck syndrome, 142ge
Brugada syndrome, 142ge
Brugia malayi, 223ge
Buerger disease, 159
Burgess shale reserves provided, 343
Burkitt lymphoma, 45, 161, 251, 339
Bypassing trials, 349

C
Caffey disease (infantile cortical hyperostosis), 142ge
CAG repeat, 71–72, 108ge, 300
Campylobacter pylori, 217
Cancer
  chemotherapy, 105ge, 157
  death rate, 161, 245, 345
  deaths, 31, 161, 167, 182, 245, 338, 340, 345
  phenotype, 9ge, 46–47, 49ge
  progression, 87, 99ge
Cancer-causing syndrome, 50ge
Candidate biomarkers, 252
Candidate causal mutations, 94
Candidate gene, 79, 99ge, 129, 303, 331
  approach, 99ge
Carbapenem resistant Klebsiella pneumoniae,
38–40
Carcinogen, 29–31, 35, 50ge, 167, 169, 175ge, 182,
243, 347
Carcinogenesis, 9ge, 26–27, 30–31, 41, 46–47, 49–50ge,
54ge, 58ge, 73, 88–89, 167, 175ge, 338, 347
Carcinoid syndrome, 142ge, 144ge
Cardiac conduction, 164
Cardiac rhabdomyoma, 102ge
Cardiofaciocutaneous syndrome, 347–348
Cardiomyopathy, 101ge, 104ge, 128, 137, 209, 247–248,
331
Cargo disorder, 175ge
Cargo receptor complex, 100ge
Cargo vesicle, 100ge
Carney complex, 95–96, 99ge, 103ge
Carney syndrome, 50ge, 99ge
Carotid body chemoreceptor cells, 81
Carriers, 28, 32–33, 50ge, 78, 96–97, 107ge, 182, 194–195,
219ge, 332–336, 339
Case studies, 99ge
Castleman disease, 45, 50ge, 331
Categorical data, 291
Catheter, 214, 219ge
Cattle, 332–333
Causality, 6, 17–61, 356–357ge
  paradoxes, 17–25
Cause of death error, 311ge
CCR5 coreceptor, 172
Cd1a, 139
CD20 inhibitors, 346, 349–350
Ceased and liver tumor, 47
Celiac disease, 208
Cell death, 3, 48, 49ge, 61ge, 80, 91, 99ge, 133–134, 197–198,
254ge
Cell death rates, 254ge
Cell division, 3, 21, 56–58ge, 60–61ge, 90, 190–191
Cell free media, 203
Cell junctions, 193
Cell suicide, 48, 49ge
Cell type, 9ge, 19–20, 26, 30–31, 35, 42–43, 48, 49ge,
51–52ge, 56–58ge, 60ge, 71, 80, 84–85, 89–90, 92,
101ge, 107–108ge, 120, 129–130, 175ge, 219ge, 222ge,
278, 281
Cellular differentiation, 89, 100–101ge
Cellular hypoxia, 81
Cellular pathway, 4, 120, 128–130, 164, 169–170, 175ge,
199
Cellulitis, 29, 213
Centers for Disease Control and Prevention (CDC),
38–40, 306–307
Cerebellar ataxia, 99ge, 128
Cerebellum, 102–103ge, 156, 159, 174ge
Cerebrospinal fluid, 79
Cervical cancer, 32, 338–340, 358ge
Chagas disease, 187–188, 194–195, 219ge, 339
Channel defects, 121
Channel disorders, 142ge
Channelopathies, 9–10ge, 124, 142ge
Charcot-Marie-Tooth disease, 51–52ge, 70, 175ge, 331
Chediak-Higashi syndrome, 10ge, 199
Chemodectomas, 81
Chemokine, 76, 100ge, 172
Chemoreceptor cell hyperplasia, 81
Chernobyl, 169
Cherubism, 199
Chicago heat wave, 295–296
Chikungunya virus, 219ge, 223ge
Childhood cancer, 27, 49ge, 161, 343, 345
Childhood thyroid cancer, 169
Chimeric Antigen Receptor for T cells (CAR-T) therapy,
161–162
Chimeric receptor, 161–162
Chimney sweeps, 166
Chimpanzee’s genome, 75–76
Chitin, 217, 242
Chlamydia trachomatis, 208
Chloramphenicol, 166
Chloroplasts, 191–192, 219ge
Cholangiocarcinoma, 30, 60ge, 166–167, 339
Chorioadenoma destruens, 255ge
Choriocarcinoma, 108ge, 161, 220ge, 251, 254–255ge,
257–258ge
Chromatin remodeling complex, 85, 89
Chromosomal aberration, 156–157
Chromosomal anomalies, 94, 335
Chromosomal instability, 49ge
Chronic myelocytic leukemia, 58ge, 161
Chronic myelomonocytic leukemia, 56ge, 104ge
Chronic obstructive pulmonary disease (COPD), 98ge,
158–159, 246, 253ge
Chronic recurrent multifocal osteomyelitis/synovitis,
198
Chryseobacterium meningosepticum, 217
Churg-Strauss syndrome, 206
Chytrids, 241–242
Cigarette smoking, 156, 173ge
Cigarette use, 158
Ciliated epithelia, 9ge
Ciliopathies, 9ge, 122–123, 124f
Circulating proteases, 82
Cirrhosis, 58ge, 82–83, 100ge, 126, 128–130, 158–159,
164–165t, 197
Cis acting, 100ge, 108ge
CJD prion, 144ge
C-KIT, 56ge, 103ge, 140
Clade, 253ge
Cladistics, 253ge, 255ge
Class Apicomplexa, 184
Class Archaeplastida, 191–192
Class assignment, 187, 205, 218ge, 221–222ge, 243–244,
252–253ge, 311ge
Class blending, 218ge, 244–247, 252ge
Class Crianata, 219ge
Classification, 5, 10ge, 59–60ge, 93–109, 183–184, 185f,
186–187, 206, 229–232, 239–244, 253ge, 269, 311ge,
341
Classifier algorithms, 243–244, 253ge
Class Mimiviridae, 221ge
Class properties, 184, 189, 231, 234–235, 256ge
Clear cell carcinoma, 106ge
Clinical trial, 5, 8ge, 136–137, 162, 239, 244, 295, 343–350
Clinorchis sinensis, 60ge
Clonal disorder, 105ge
Clonal expansion, 31, 56ge, 102ge, 105ge, 107ge, 134,
248–249
Cluster analysis, 253ge
Clustered Regularly Interspaced Short Palindromic
  Repeats (CRISPR) technology, 335–336
Coagulopathies, 235
Coding
  mutations, 99ge, 119, 126, 137, 140, 158–159, 281
  regions, 158–159
Codon, 56ge, 106ge, 287–288, 311ge
Coelomic, 237, 243
Colchicine, 200
Collagen disorder, 142ge
Collagenopathy, 128, 142ge, 164
Combined DNA Index System (CODIS), 265
Commensal organism, 214
Common ancestor, 102ge, 105ge, 187, 221–222ge
Comparing genome sequences, 265
Complement disorders, 199
Complement regulatory diseases, 198
Completeness, 230, 272, 276
Complex disease, 54ge, 58ge, 173ge, 201, 220ge, 352
Complexity barrier, 299–300
Conduction disorders, 164
Congenital anomaly, 51ge, 94, 355ge
Congenital disorder, 24–25, 51ge, 58ge, 164, 173ge, 175ge
Congenital dyserythropoietic anemia, 254ge
Congenital heart defects, 122–123
Congenital hyperinsulinism, 142ge
Congenital tumor, 155–156
Conserved gene variants, 157–158
Contig disease, 355ge
Contiguous gene deletion syndrome, 355ge
Control population, 298, 358ge
Control samples, 174ge
Control subjects, 99ge, 281
Converged pathway, 137, 140, 239
Convergence, 4–5, 8ge, 73–74, 117–145, 187, 200,
221–222ge
Convergent disease, 135–145, 340, 346, 350
COPD. See Chronic obstructive pulmonary disease
(COPD)
Copy neutral, 109ge
Copy number, 47, 51ge, 57ge, 102ge, 222ge
Coronary artery disease, 38, 143ge, 182
Coronary artery thrombosis, 38
Corpora amylacea, 126
Corpus callosum, 159
  and alcoholism, 159
Correlation, 96, 107ge, 121, 267, 315ge, 352
  distance, 311ge
Cost of curing curable disease, 141
Cowden syndrome, 50ge, 95, 100ge, 102ge, 156,
173–174ge
Cowdry bodies, 280f, 281
Coxsackie virus, 209
CpG islands, 84, 87, 100ge, 109ge
CpG methylation patterns, 100ge
CpG sites, 84, 90, 100ge
Cranioectodermal dysplasia (Sensenbrenner syndrome),
122–123
Creutzfeldt-Jakob disease, 144ge, 219ge
Cri du Chat, 104ge
CRISPR technology. See Clustered Regularly Interspaced
Short Palindromic Repeats (CRISPR) technology
Crohn disease, 54ge, 99ge, 199, 201, 220ge
Curator, 302, 307–308, 311ge
Curse of dimensionality, 296, 311ge
Cutis laxa, 72, 134, 289, 354ge
Cyanobacteria, 191–192, 219ge
Cyclooxygenase–2, 24
Cyclospora, 184, 219ge
Cyst, 104ge, 213, 216–217
Cystic fibrosis, 71, 98ge, 141, 142ge, 160, 334, 351, 356ge
Cytokine signaling, 83, 198
disorders, 199
Cytokine storm, 140, 352–353
Cytomegalovirus, 182, 222ge, 281
infection, 280f
Cytotoxic agents, 87, 105ge
Cytotoxic lymphocytes, 143ge
Cytotrophoblasts, 193, 238

D
DARC, 172
Darier disease, 24, 26–27
Dark data, 311ge
Data
  mining, 230, 355ge
  object, 9–10ge, 13ge, 143ge, 243–244, 253–258ge,
  264–271, 273–275, 285–286, 290–291, 298,
  311–314ge, 316–318ge, 355ge, 357–358ge
  readers, 341
  repurposing, 312ge, 355ge
  resource, 122–123, 252, 254ge, 271–272, 284, 286,
  292–293, 341, 352
  scrubbing, 307, 312ge, 355–356ge
  sharing, 302–318, 312ge, 343, 349
  triple, 267
Death certificate, 13ge, 38–40, 39f, 311ge, 339
Dedifferentiation, 101ge
Deficiency of the interleukin-1 receptor antagonist, 198
Deidentified record, 275, 308
Dengue virus, 223ge
De novo mosaic disease, 336
De novo mutation, 12ge, 24, 51ge, 57ge, 101ge, 107ge,
331–332, 334–336, 356ge
Dentatorubropallidoluysian atrophy, 108ge
Deoxyadenosine deaminase deficiency, 80
Deoxycoformycin, 80
Dermal bone, 254–255ge
Dermatofibroma, 45
Developmental disorder, 7, 75, 105ge, 139, 154–155t, 156
Diagnostic and Statistical Manual of Mental Disorders
(DSM), 7–13
Diethylstilbestrol fulfill, 30–31
Differentiation, 55ge, 80, 89, 100–101ge, 108ge, 243,
349–350
Digenic disease, 105ge, 173ge
DiGeorge syndrome, 104ge, 131
Dilated cardiomyopathy, 70, 95, 122–123, 128, 143ge
Dimensionality, 253ge, 296, 312ge, 356ge
Discordant diagnoses, 247–248
Disease convergence, 4–5, 8ge, 73, 117–145, 187, 200,
221–222ge
Disease pathway, 3, 40–44, 105ge, 142–145, 163, 170,
349–350, 353
Diseases-in-waiting, 205–210
Disease variants, 73, 78, 123–124, 132, 303, 331–332
Distal myopathy, 98ge, 279
Diuretics, 121, 136
DNA crosslink repair, 42, 51ge
DNA repair, 22, 34, 41–42, 48, 49ge, 51ge, 61ge, 72, 80, 84,
91, 99ge, 144ge, 164
DNA replication, 51ge, 144ge
Donovania granulomatis, 217
Double stranded DNA, 186–187, 221ge
Down syndrome, 52ge
Dravet syndrome, 142ge, 303
Driver pathway, 9ge, 170, 174ge
Drug-induced methemoglobinemia, 128
DSM. See Diagnostic and Statistical Manual of Mental
Disorders (DSM)
Ductal carcinoma in situ (DCIS), 32, 32f, 95
Duffy blood group antigen, 172
Dynein arms, 123
Dyserythropoiesis, 250, 254ge
Dyslipidemias, 164, 172
Dysmorphic facial development, 33–34
Dysmorphism, 121–122, 127, 156, 170, 234
Dysplasia, 51ge, 53ge, 70, 104ge, 122–123, 136f, 156–157,
175ge, 358ge
Dysplastic gangliocytoma, 102ge, 156
Dysplastic nevi, 33, 95
Dysregulations, 88, 106ge, 235
Dystrophic epidermolysis bullosa, 134
Dystrophin, 122

E
Early detection, 248–249, 251–252
Eaton lambert syndrome, 144ge
Ebola virus, 210
Ectoderm, 58ge, 61ge, 88, 101–102ge, 104ge, 235–238, 255ge
Edematous placental villi, 220ge
Eearly onset sarcoidosis, 54ge, 220ge
Ehlers-Danlos syndrome, 142ge
Electronic health record, 341
Electronic medical record, 272–273, 276, 340
Ellis van Creveld syndrome, 122–123
Embryo, 3, 30–31, 34, 54ge, 59ge, 85, 92, 103ge, 107ge, 235,
238, 252, 254–255ge
Embryogenesis, 81, 86, 103ge, 255ge
Embryonal carcinoma, 161, 238, 255ge
Emery-Dreifuss muscular dystrophy, 330
Emphysema, 82–83, 134, 158–159, 164–165t
Encapsulation, 308, 310, 312ge
Encephalitis lethargica, 206
Encephalitozoon intestinalis, 217
Enchondral bone, 238, 254–255ge
Encryption, 276, 312ge
Endocarditis, 202–203, 213, 220ge
Endoderm, 58ge, 61ge, 101ge, 104ge, 142ge, 235–238, 252ge,
255ge
Endodermal/Ectodermal neoplasms, 236
Endometrial carcinoma, 156
Environmental disease, 22, 83, 208
Environmental factors, 11–12ge, 24, 97, 119, 121, 127, 129,
158, 173, 173ge
Eosinophilic granulomatosis, 206
Epidermis, 30, 48, 53ge, 58ge, 60ge, 73, 101ge, 107ge,
123–124, 132, 242
Epidermolysis bullosa, 8ge, 70, 73, 123–124, 134
Epigenetic instability, 87, 108ge
Epigenome, 47–48, 51ge, 84–93, 89f, 100–101ge, 103ge,
108ge, 130, 154–155t
Epigenomic disease, 85, 90, 92
Epigenomics, 47–48, 84–93, 100–101ge, 129–130,
154–155t, 252ge
Epistasis, 6, 9ge, 42–43, 57ge, 144ge
Epithelial and non-epithelial tumors, 243
Eponymous disease, 45, 52ge
Epstein-Barr virus, 45, 55ge, 79, 140, 251, 339
Erasure, 85, 91, 103ge
Esophageal cancer, 159
ETV6-NTRK3 fusion, 250
Eugenics, 19, 331–337
Eukaryote, 55ge, 57ge, 182–183, 191–192, 205, 213, 219ge,
241, 354
Evolution, 48, 190–193, 230–231, 327–328, 343, 354
Evolutionary taxonomy, 184
Evolve, 8ge, 41–42, 60ge, 191–192, 306
Exceptional responders, 275, 295, 348–349
Exceptional responder trials, 348
Exome sequencing, 78–79, 98
Exon, 98ge, 101ge, 106ge
Exotic disease, 181, 219ge
Expressed genes, 109ge, 292
eXtended Markup Language (XML), 264–265, 317ge
EYA4 gene, 95

F
Facial angiofibromas, 102ge
Factor VIII, 56ge, 100ge, 134, 172
Facultative intracellular organism, 190–191, 220ge
Fair use, 307, 312ge
Familial aneurysm disorders, 142ge
Familial cold autoinflammatory syndrome, 54ge,
198–199, 220ge
Familial cold autoinflammatory syndrome-2
  (Guadaloupe periodic fever), 199
Familial dilated cardiomyopathy, 70, 95
Familial HLH, 199
Familial hypercholesterolemia, 56ge, 59ge, 164–165t, 171
Familial hypocalciuric hypercalcemia type, 134
Familial mediterranean fever, 54ge, 198, 200, 220ge
Fanconi anemia, 42, 49–51ge
Father’s sperm, 11ge
Ferruginous bodies, 279–280
Fibrochondrogenesis, 70
Fibromyalgia, 206
Fibrosing disorders, 199
Fibulin, 72
Filaria, 208, 218
First analyses, 303, 310
Flagellum, 199, 241–242
Flattening, 48, 341, 354
Flawed data, 307–308
Fleas, 202
Flu epidemic, 294, 332–333
Fluke, 48, 182, 339
Folic acid supplementation, 121
Food and Drug Administration, 141, 160–161, 346
Foreign antigen, 21–22, 192, 218ge
Forme fruste, 165, 174ge
Founder effect, 332–333, 356ge
Founder’s mutation, 356ge
Fourier series, 288, 357ge
Fragile x syndrome, 99ge, 108ge, 355ge
Free-lance data analysts, 343
Frequency, 34–36, 40, 57–58ge, 106–107ge, 125–127, 131,
155, 170, 189, 291, 292f, 300, 310, 318ge, 357ge
distribution, 291
Freshwater, 194
Friedreich ataxia, 83, 99ge, 108ge, 143ge
Frontotemporal dementia, 77, 281, 354ge
Fungemia, 212

G
GAA repeat, 83
Gain-of-function, 119
Gamete, 102–103ge, 174ge, 238, 255ge, 336
Gastric maltomas, 49ge
Gastrointestinal stromal tumor (GIST), 103ge, 140
Gastrulation, 236–238
Gaucher disease, 280, 334
Gaussian curve, 358ge
Gaussian distribution, 288, 291
Gene duplication, 105ge
Gene editing, 327–328, 335–336
Gene pool, 18, 35–36, 119, 157–158, 183–184, 190–191,
331–336
Gene sequence, 42, 161, 187, 204–205, 290
Gene silencing, 86–87, 336
Gene therapy, 46, 160–161
Genetically engineered mouse (GEM), 52ge
Genetic disease, 11ge, 22, 69–70, 120, 160, 208, 235, 335
Genetic heterogeneity, 8ge, 71, 74, 101ge, 103ge, 144ge
Genetic instability, 47, 49ge, 87–88, 89f, 92–93, 95, 99ge,
102ge, 175ge, 357ge
Genetic mosaicisms, 336
Genetic mutation, 2, 7ge, 11ge, 24–25, 72, 74–75, 80, 85, 90,
97, 118, 125, 127, 139, 145ge, 171, 175ge, 303
Genetic testing, 71
Genome testing, 97
Genome-wide Association Study (GWAS), 174ge
Genotype, 9ge, 23, 52ge, 60ge, 97, 99ge, 103ge, 144ge, 157,
175ge, 183, 215
Geriatric disease, 233
Germ cell(s), 52ge, 55–57ge, 59ge, 61ge, 102–103ge, 107ge,
155–156, 174–175ge, 235, 238, 251, 255ge, 290, 336,
339
  of body, 52ge
  line, 255ge, 336
  neoplasms, 238
  origin, 238, 290
  tumors, 103ge, 155–156, 238, 251, 339
Germinomas and seminomas, 175ge, 238, 255ge
Germ layer, 85, 104ge, 236–238, 242–243
Germline, 7ge, 25, 27–28, 33–34, 49ge, 51–52ge, 54ge, 60ge,
75–76, 80–82, 85–86, 97, 101ge, 103ge, 107ge, 109ge,
119, 126, 128, 130, 156, 220ge, 255ge, 313ge, 335–336
Germline mutation, 7ge, 25, 27, 33–34, 49ge, 51–52ge,
75–76, 80–82, 102ge, 107ge, 119, 130, 156
Gerstmann Straussler Scheinker Syndrome (GSS), 144ge
Gestational mole, 220ge
Gestational trophoblastic disease, 255ge
Giant cell (temporal) arteritis, 206
Gilbert syndrome, 57ge
GIST. See Gastrointestinal stromal tumor (GIST)
Glioblastoma, 138, 156
Gluten sensitive idiopathic neuropathies, 206
Glycosylation disorder, 58ge, 164, 173ge, 175ge
Gnathostomes, 192–193, 218ge
Golgi bodies, 41
Goodpasture syndrome, 132–133
Gorlin syndrome, 170, 175ge
Gout, 124, 198–201, 351–352
Granulocyte/macrophage colony stimulating factor
(GM-CSF), 137–138, 346
Graves’ disease, 131–132
Grawitz tumor, 282–283
Griscelli syndrome, 175ge, 199
Group A streptococcus infection, 21–22, 203
Gulf War syndrome, 206
GWAS. See Genome-wide Association Study (GWAS)

H
HACEK, 203, 213, 220ge
Haemophagocytic lymphohistiocytosis, 140, 199
Hairy cell leukemia, 55ge, 161
Hallermann Streiff syndrome, 206
Hamartin, 72, 103ge, 139, 354ge
Hamartoma, 56ge, 72, 100ge, 102ge, 138–139, 156, 174ge,
234, 354
Haploid, 86, 102ge
human genome, 287
Haploinsufficiency, 75
Haplotype, 86, 102ge
Hashimoto thyroiditis, 134
Healthcare institutions, 316ge
Hearing loss, 70, 82, 104ge, 106ge, 123, 128, 135,
173ge
Heat related deaths, 295–296
Heavy legs, 206
Helicobactor pylori infection, 49ge
Hematopoietic tissue, 166, 254ge
Hemochromatosis, 128, 143ge
Hemoglobin, 42, 56ge, 105ge, 107ge, 119, 143ge, 160, 163,
195–196, 334–335, 354ge
synthesis, 195–196
Hemolytic anemia, 133–134, 144ge, 197–198
Hemolytic uremic syndrome, 135, 199
Hemophagocytic lymphohistiocytosis, 99, 199
Hemophilias, 164, 235
HEMPAS, 254ge
Henoch schonlein purpura, 206
Hepatic angiosarcoma, 166–167
Hepatitis B virus, 339–340
Hepatitis C virus, 339
Hepatocellular carcinoma, 30, 47, 52ge, 58ge, 83, 166–167,
182, 339–340
Herceptin, 348
Hereditary angioedema, 126, 135
Hereditary hemorrhagic telangiectasia, 138
Hereditary inclusion body myopathy, 98ge
Hereditary leiomyomatosis, 79
Hereditary nonpolyposis colorectal cancer syndrome
(HNPCC), 143ge
Hereditary thrombocythemia, 98ge, 105ge, 139, 165
Hermansky-Pudlak syndrome, 175ge, 199
Herpes simplex infections, 194–195, 281
Heterochromasia iridum, 289
Heterokont, 213
Heterotaxy, 72, 105ge, 123
Heterozygosity, 54ge, 103ge, 109ge
High blood pressure, 44, 136
Hispaniola, 189, 196
Histiocytic medullary reticulosis, 103ge
HIV, 100ge, 128, 172, 196, 199, 221ge
and AIDS, 210–211, 219ge
Hodgkin lymphoma, 45, 52ge, 70, 161, 292–293, 293f,
357ge
Holt-Oram syndrome, 61ge, 76, 145ge
Homolog 1, 130
Homologous genes, 184
Homologs, 105ge, 354
Homology, 214, 253ge
Homunculus, 3
HTLV-1, 339
HTT gene (and CAG repeat), 71–72
Human malaria, 215
Human subject, 275–276, 294–295, 301, 307–308, 310,
345–346, 348, 356ge
Huntingtin protein, 71–72
Huntington disease, 71–72, 99ge, 103ge, 108ge, 300, 354ge
Hurler disease, 282
Hydatidiform mole, 198–199
Hydrops-ectopic calcification-‘moth-eaten’ (HEM), 96
Hydrops fetalis, 96, 131
Hyperbilirubinemia, 57ge
Hypercholesterolemia, 127, 171–172
Hyperimmunoglobinemia D with periodic fever
syndrome, 198
Hyperimmunoglobulin D, 54ge, 220ge
Hypermethylation, 86–87, 130
Hypermutate, 134
Hypernephroma, 282–283
Hypersurveillance, 327–329
Hypertension, 24–25, 121, 128, 136–137, 136f, 139, 158,
162, 165, 174ge, 334
Hyperthermia, 142ge, 144ge
Hypertrophic osteoarthropathy, 10ge
Hypocalciuric hypercalcemia, 284
Hypomethylating agents, 86–87, 91

I
ICD. See International Classification of Disease (ICD)
Ichthyosis, 104ge
Identification, 143ge, 160, 204, 234, 240–241, 253ge, 265,
271–277, 286, 308, 313ge, 338
Identifier, 13ge, 174ge, 253ge, 265, 355ge
IL-1-beta activation disorders, 198
Imatinib, 4, 140–141, 161, 346
Immediate cause, 37–38
Immune deficiency, 194, 222ge
syndromes, 192–193, 197, 218ge
Immunocompromised individuals, 194, 213
Immunodeficiency, 46, 72, 76, 79–80, 84, 98ge, 332–333,
352
Immunosuppressed individuals, 45
Immutability, 270–271, 273
Imprinting, 86
Inactivated x chromosome, 279
Incidental finding, 213
Inclusion bodies, 279–281
Incomplete dominance, 144ge
Indexing, 277, 281
Infectious disease, 7ge, 34, 36, 50ge, 53ge, 154, 181–190,
194–196, 202–204, 209–211, 213–214, 216, 219ge,
222ge, 231, 233–234, 251, 339, 352, 356ge
Inflammasome, 198, 200, 207
Inflammasomopathies, 198
Inflammatory disease, 54ge, 94, 139, 196–201, 220ge,
251
Inflammatory myofibroblastic tumor, 346
Inflammatory pathway, 139, 196–197, 200–201
Inflammatory response syndrome, 8ge
Information is release, 305
Informed consent, 346
Inherited dilated cardiomyopathy, 171
Inherited retinoblastomas, 313ge
Initiation, 45, 50ge, 54ge, 101ge, 145ge
Initiation factor, 56ge, 145ge
Innate immune system, 138, 197–201
Inscrutable genes, 69–78
Institutional Review Boards (IRBs), 275–276, 307, 346
Intermediate cause, 37–38
Internal body spaces, 237
International Classification of Disease (ICD), 233–234
Interoperability, 282, 306
Interpretation errors, 300
Interstitial cystitis, 206
Intracellular, 134, 190–191, 195, 199–200, 217, 220–222ge,
279
organisms, 195
Intraductal hyperplasia, 32
Intraflagellar transport, 123
Intrinsic factor, 135
Intrinsic immunity, 281
Introns, 83
Introspection, 269–270
IRBs. See Institutional Review Boards (IRBs)
Irreproducible, 6, 216, 218ge, 252ge, 298–300, 309
Irreversible, 126, 275–276
Irritable bowel syndrome, 206, 213

J
JAK2, 105ge, 139–140, 165
JAK2 mutation, 56ge, 140, 165
JAK2V617F, 140
Janus kinase, 72
Jeune chondrodysplasia syndrome, 122–123
Joubert syndrome, 122–123

K
Kallman syndrome, 98ge, 104ge
Kaposi sarcoma, 45–46, 50ge, 210–211, 293, 339
Karyopyknosis, 49ge
Karyotype, 89f, 103ge
Kashin-Beck disease, 206, 209
Kawasaki disease, 206
Kelley-Seegmiller syndrome, 105ge
Keratoacanthoma, 45, 49ge, 54ge
Kingella kingae, 203, 220ge
Klebsiella granulomatis, 217
Knockout mice, 27, 54ge, 128, 143ge, 352
Koch’s postulates, 201–205, 352, 356ge
Koilocytosis, 280f
Korean War, 286–287
Kupffer cells, 166–167

L
LAMA4 gene, 95
Lamellar inclusions, 137
Lamina propria, 203–204, 208
Lamin B receptor, 96
Lamin gene, 330–331
Large follicular center cell (diffuse histiocytic)
lymphoma, 161
Lassa fever, 219ge
Latency, 29–30, 46–47, 54ge
Lead-induced encephalopathy, 128
Leber congenital amaurosis, 122–123
Legacy data, 270, 273, 306, 314ge
Leishmaniases, 194–195, 342
Leishmaniasis, 154, 187–189, 219ge, 223ge
Leri-Weill dyschondrosteosis, 53ge
Lesch-Nyhan disease, 351–352
Lethal acantholytic epidermolysis bullosa, 70
Lethal genotypes, 144ge
Lethal junctional epidermolysis bullosa, 6, 70
Leukemia, 4, 9ge, 28, 33–35, 46, 49ge, 55–56ge, 58ge, 73, 87,
95–97, 103–104ge, 139–141, 142ge, 155–157,
160–162, 166, 233–234, 251, 339, 346, 349–350,
354ge
Lewy bodies, 126, 279
  dementia, 279
Lichen sclerosus, 206
Liddle syndrome, 24–25, 136f, 165
Li-Fraumeni syndrome, 49–50ge, 73, 103ge
Ligneous conjunctivitis, 79, 354
Limb malformation, 75, 168
Lineage, 9–10ge, 49ge, 51–52ge, 55–56ge, 84–85, 88,
100–103ge, 105ge, 107–108ge, 131, 185f, 230–232,
235, 242–243, 252–253ge, 255–257ge, 314ge, 318ge,
358ge
Lipodystrophy, 70–71, 172, 330–331
Lipopolysaccharide, 353
Liver cancer, 28, 47–48, 52ge, 159, 251, 340
LMNA (Lamin A/C), 70–71, 331
LMO2, 46
Locus heterogeneity (LOH), 71–72, 101ge, 103ge, 120, 141
Long branch attraction, 187, 221–222ge
Long noncoding RNA, 106ge
Loss-of-function, 98–99ge, 118, 158–159, 197–198
Lou Gehrig disease, 282
Louis-Bar syndrome, 99ge
Low penetrance, 27, 50ge
LRRK2 gene mutation, 164–165t, 171
LRRK2 protein kinase, 171
Lubeck disaster, 22
Lymphoma, 28, 45–47, 49–50ge, 52ge, 55ge, 88, 103ge, 139,
155–157, 161, 251, 292–293, 293f, 339–340, 346,
349–350, 356–357ge
Lymphoproliferative disease, 45, 55ge
Lymphoproliferative disorder, 50ge, 56ge
Lynch cancer family syndrome, 50ge
Lynch syndrome, 119, 143ge
Lytico-Bodig disease, 206

M
Machine translation, 255ge, 282–283
Macrophage, 23, 54ge, 137–138, 140, 195, 198–199, 201,
203–204, 220ge, 280–281, 346
activation disorders, 199
Mad hatter disease, 168
Majeed syndrome, 54ge, 198, 220ge
Major histocompatibility complex, 72
Malakoplakia, 281
Malaria, 107ge, 169, 172, 182, 184, 195–196, 215, 219ge,
223ge, 244–245
Malassezia furfur, 217
Malignant fibrous histiocytoma (MFH), 218ge, 252ge
Malignant phenotype, 28, 47–48, 50ge, 55ge, 87, 99ge,
105ge, 107ge, 175ge
Malignant transformation, 26–27, 238
Malnutrition, 219ge, 222ge
MALToma, 30, 48, 357ge
Maltomas, 49ge, 55ge
Mantle zone, 1
Manual coding, 283
Marchiafava-Bignami disease, 159
Martsolf syndrome, 175ge
Materia medica, 200
Maternal lineage, 55ge
Mathematical models, 356ge
Maturity onset diabetes of the young (MODY), 76, 96–97
Mayaro fever virus, 219ge
McKusick-Kaufman syndrome, 122–123
Meckel-Gruber syndrome, 122–123
Mediterranean anemia (thalassemia), 200
Medullary, 81, 103ge
Medullary thyroid carcinoma, 237, 255ge
Medulloblastoma, 103ge
Melanoma, 26–27, 33, 55ge, 94–95, 103–104ge, 107–108ge,
237, 288
MEN2 (multiple endocrine neoplasia-2), 256ge
Mendelian disease, 104ge, 131
Mendelian inheritance, 104ge, 131–132, 144ge, 154
Menetrier disease, 53ge
Merkel cell carcinoma, 339
Mesenchymal cells, 242–243
Mesoderm, 58ge, 60–61ge, 88, 101ge, 104ge, 158, 235–238,
242–243, 255ge
Mesothelioma, 26–27, 29, 126, 167–168, 339
Metabolic pathway, 24, 117, 120, 125, 159, 191, 235, 346,
348
Metabolic syndrome, 158, 174ge
Metadata, 264, 267–270, 286, 311ge, 316ge
Metastasis, 9ge, 144ge, 221ge, 251, 255ge
Metazoa, 230, 255ge
Methemoglobinemia, 128
Methotrexate, 128
Methylating agents, 86–87, 91
Methylation, 75, 84, 86–87, 90–91, 100–101ge, 103ge
inhibitors, 91
Methylmalonic acidemia, 128
MFH (malignant fibrous histiocytoma), 218ge, 252ge
MGUS (monoclonal gammopathy of undetermined
significance), 31, 55ge, 248–249
Microarray, 13ge, 336, 342, 357ge
Microdeletion disease, 104ge
Microphthalmia-, 53ge, 61ge, 82, 145ge
MicroRNA, 85, 91–92, 104ge, 106ge
Microsatellite, 57ge, 98ge, 143–144ge
  instability, 119, 140, 144ge
Microscopic polyangiitis, 207
Microthrombotic disorder, 125
Miller syndrome, 128
Milroy disease, 75, 96–97, 120
Mimics of common diseases, 129, 131, 248
Mimics of rare inherited disease, 131
MiRNA, 104ge
Misfolded proteins, 98ge, 144ge
Mismatch repair, 42, 51ge, 130, 140, 143–144ge
  pathway, 130
Mitochondrial DNA, 73, 77
Mitochondrial myopathy, 128
Mitochondriopathic deafness, 128
Mitochondriopathies, 84, 104ge, 164
Mitosis, 55–56ge, 109ge
Modeling algorithms, 357ge
MODY (maturity onset diabetes of the young), 76, 96–97,
121, 164–165t
Monkeypox virus, 219ge
Monoclonal gammopathy, 31, 50ge, 55ge, 248–249
Morgellons disease, 207
Mortimer disease, 207
Motor neuron disease, 104ge, 279
Muckle-Wells syndrome, 54ge, 198, 220ge
Mucolipidosis, 142ge
Muir-Torre syndrome, 50ge
Multicentric Castleman disease, 50ge
Multiclass classification, 230, 255ge
Multiclass inheritance, 50ge, 256ge
Multifactorial disease, 103ge
Multiple basal cell carcinoma, 175ge
Multiple endocrine neoplasia, 256ge
Multiple myeloma, 31, 55–56ge, 248–249, 249f
Multiple sclerosis, 197, 201, 349–350
Multipotent stem cell, 56ge
Multi-step disease, 29–30
Multistep pathogenesis, 12ge, 205
Muscular dystrophy, 53ge, 56ge, 70, 122, 175ge, 330
Muscularis propria, 265
Mutagen, 56ge
Mutation, 2, 23, 69–70, 118, 156, 187, 250, 281, 329
Mutation rate, 336, 357ge
Mutator phenotype, 156–157, 175ge
Myalgic encephalomyelitis, 346, 349–350
Myasthenia gravis, 126, 133, 142ge, 197
MYC, 47–48, 95
Myelodysplastic syndrome, 104ge, 156–157, 250
Myelofibrosis, 56ge, 139, 165
Myeloproliferative disorders, 33–34, 56ge, 60ge, 104ge,
124, 139–140, 165
Myeloproliferative syndrome, 48ge
Myofascial pain syndrome, 207
Mytotonic dystrophy, 108ge
Myxomatosis, 350–351

N
Naegleria encephalitis, 194, 213
Naegleria fowleri, 194, 213
National Academies of Sciences, 1
National patient identifier, 276–277, 314ge
Natural selection, 35–36, 41, 43, 56ge, 58ge, 157–158, 169,
191–192, 334, 336
Neonatal-onset multisystem inflammatory disease,
  54ge, 220ge
  chronic neurologic cutaneous and articular syndrome,
  198
Neorickettsia sennetsu, 210–211, 218
Nephroblastomas, 30, 243
Nephronophthisis, 122–123, 351–352
Nerve cells, 143ge
Nerve conduction, 82
Neural crest, 81–82, 84, 219ge, 235, 237–238, 254–256ge
Neural tube, 237–238
  defects, 121
Neurectoderm, 88, 105ge
Neuroblastoma, 75, 95, 155–156, 293, 346
Neurocristopathy, 238, 256ge
Neurofibrillary tangles, 126, 353
Neurofibroma, 56ge, 74–75, 313ge
Neurofibromatosis, 27, 56–57ge, 74–75, 104ge, 109ge, 138,
145ge, 256ge, 313ge, 354ge
Neuromuscular junctions, 138
Neuron, 51ge, 84, 100ge, 104ge, 107ge, 279
Neurotoxin, 300
Neutralizing autoantibodies, 137–138
Neutropenia, 49ge, 76, 83, 100ge, 133–134, 137, 197–198,
346
Nevocellular cells, 57ge
New daily persistent headache, 207
New variant creutzfeldt jakob disease, 144ge
NF-B activation disorders, 199
NKX2.5 homeobox gene, 53ge
Nodding disease, 207–209
Nomenclature, 10ge, 12ge, 38, 59ge, 61ge, 216, 221ge,
223ge, 233, 241, 257ge, 282–284
Nomenclature code, 282–283
Noncoding region, 76–77, 101ge, 106ge, 118, 127, 158–159,
336
Nonconvergent disease, 124
Nonhistone chromatin complex, 84
Nonhistone nuclear protein, 88
Non-Hodgkin lymphoma, 349–350, 357ge
Noninherited genetic disease, 33–34, 57ge
Nonphylogenetic property, 187, 222ge
Nonphylogenetic signal, 187, 222ge
Nonquantitative data, 277–279
Nonsyndromic disease, 70, 105ge
Nuclear atypia, 19–20, 55ge, 87–88, 89f, 102ge
Nuclear membrane, 41, 87–88, 109ge
Nucleoli, 41, 87–88, 89f
Null hypotheses, 106ge, 314–315ge, 318ge
NUT-1 Gene, 164–165t
NUT midline carcinoma, 250

O
Obesity, 11ge, 54ge, 72, 75, 103ge, 123, 143ge, 157–158, 162,
174ge, 337
Object oriented programming, 223ge, 254ge, 256ge,
266–267, 269, 311–312ge, 314ge
Obligate intracellular organism, 190–191, 222ge
Observational data, 349, 358ge
Odontogenic keratocyst, 170, 175ge
Off-label, 350, 358ge
Oguchi disease, 73
Oligodendroglioma, 358ge
Onchocerca volvulus, 208, 219ge
Oncocyte, 159, 176ge
Oncogene, 26–28, 34–35, 46–47, 48ge, 54ge, 57ge, 59ge,
80–81, 107ge, 143ge, 192, 222ge, 329, 357ge
Ontology, 6, 9ge, 60ge, 232, 256ge, 306, 311ge
O’nyong’nyong fever virus, 223ge
Oocyte, 11ge, 52ge, 55ge, 107ge, 155
Open access, 303
Open reading frame (ORF), 70, 105ge
Open source, 288, 290–291, 313ge
Operating system, 232, 267, 272, 288, 313ge, 317ge
Opisthokonts, 217, 241–242
Opisthorchis viverrini flatworm (fluke), 339
Opportunistic infection, 194, 205, 222ge
Oral cancer, 159
ORF (open reading frame), 70, 105ge
Orofaciodigital syndrome, 122–123
Orphan diseases, 154
Orphan drug, 160, 175ge, 345–346
Orthodisease, 354, 358ge
Ortholog, 75–76, 105ge, 354
Orthologous gene, 105ge, 351, 354, 358ge
Osteitis fibrosa cystica, 109ge
Osteoarthritis, 94, 209
deformans endemica, 209
Osteomyelitis/synovitis, 198
Osteoporosis, 168–171
pseudoglioma syndrome, 170–171
Osteosarcoma, 73, 103ge
Outcome data, 275, 290, 348–349
Outlier, 99ge, 275, 294–295, 302
Ovarian cancer, 103ge, 136–137, 344
Oxygenic photosynthesis, 191–192

P
P53, 41, 73, 95, 103ge
Palindrome, 290
Pancreatic islet, 20, 339
Pancytopenia, 49ge, 100ge, 104ge, 254ge
PAPA. See Pyogenic arthritis, pyoderma gangrenosum,
and acne (PAPA)
Papanicolaou smear, 358ge
Pap smear, 338, 358ge
Paradox, 17–25, 231–233, 246–247, 256ge
Paraganglia, 81
Paraganglial tumor, 81
Paraganglioma, 80–81, 83–84
Paralog, 105ge
Paraneoplastic syndrome, 142ge, 144ge
Paraphyly, 255ge
Parasite, 53ge, 133, 172, 182, 194–196, 215, 217, 219–220ge,
223ge, 304
Parasitic organisms, 190–191, 223ge
Parasympathetic, 135
Parent class, 8–9ge, 12ge, 189, 205, 222ge, 229–232, 252ge,
255–256ge, 267, 269, 311ge, 314ge
Pareto’s principle, 314–315ge, 318ge
Parkin knockout mice, 352
Parkinson disease, 6–7, 52ge, 54ge, 143ge, 164–165t, 171,
352–353
Paroxysmal nocturnal hemoglobinemia, 60ge
Partial lipodystrophy, 330
Partial mole, 220ge, 257ge
Partington syndrome, 53ge
Pathway, 2, 18, 69–70, 117, 159, 184, 231, 269, 341
Pathway directed treatments, 135–145, 346–347
Pathway trials, 346
Patient confidentiality, 305
PDGFR. See Platelet derived growth factor receptor
(PDGFR)
Pelger-Huet anomaly, 88, 96, 128, 355ge
Pembrolizumab, 140
Pemphigus, 132
Penetrance, 9ge, 27, 50ge, 57ge, 74, 144ge
Percolozoan encephalitis, 213
Periodic fever syndrome, 198
Pernicious anemia, 128–130, 132–133
Peroxisome biogenesis disorder, 164
Peroxisome disorder, 222ge
Personalized medicine, 2, 337
Petriellidium boydii, 216
Phakoma, 354
Pharmacogenomics, 305, 315ge
Phenocopy disease, 105ge, 125–131, 145ge, 164, 352
Phenotype, 2, 23, 71–72, 117, 156, 197–198, 235, 303, 336
Phenotypic heterogeneity, 162, 175ge
Phenylketonuria, 56ge, 160
Phenytoin embryopathy, 121–122
Pheochromocytoma, 80–81, 83
Philadelphia chromosome, 58ge
Phocomelia, 83, 128, 168
Photosynthesis, 191–192, 219ge
Phyllodes tumor, 73
Phylogenetic chronometer, 71
Phylogenetic classification, 358ge
Phylogenetics, 72, 187, 217, 221–222ge, 253ge, 278, 358ge
Phylogeny, 340, 358ge
Pigmented villonodular synovitis, 207
Pityriasis rosea, 207
PiZZ variant, 83
Placenta, 192–193, 238–240, 255ge, 258ge
Plasma cells, 31, 55–56ge, 134, 197, 218ge, 248–249, 249f
Plasminogen deficiency, 354
Plasmodium knowlesi, 215
Plasmodium vivax malaria, 172, 196
Platelet derived growth factor receptor (PDGFR), 103ge,
140
PLEC gene, 73, 123–124
Pleiotropic, 72, 76, 105ge, 332–333
Pleiotropy, 144ge
Plod2 gene (procollagen lysine dioxygenase 2), 142ge
Pneumococcal disease, 164–165t, 195
Pneumocystis, 53ge, 216–217
Point mutation, 56–57ge, 59ge, 143ge, 331, 357ge
Polyangiitis, 206–207
Polyarteritis nodosa, 207
Polycystic kidney disease, 122–123, 174ge
Polycythemia, 56ge, 83, 105ge, 139, 165
Polygenic disease, 127, 131, 144ge, 157–158, 173–174ge,
333–334
Polymorphism, 8ge, 42, 54ge, 58ge, 78–79, 97, 102ge, 106ge,
118, 134, 143–144ge, 157–158, 174ge, 195–196,
220ge, 290, 312ge
Polynucleotide repeat disorder, 108ge
Polyvinyl chloride, 166
Population of the U.S, 11ge, 107ge, 154, 291
Porphyria cutanea tarda, 57ge, 127
Posterior cortical atrophy, 207
Posterior flagellum, 241–242
Postmitotic cell, 48, 58ge, 60ge, 98ge, 234
Post translational defects, 173ge
Post translational modifications, 26, 58ge, 101ge, 164,
175ge
Post-transplant lymphoproliferative disease, 55ge
Potocki-Shaffer syndrome, 53ge
Power law, 288, 291, 314–315ge, 356–357ge
Power series, 288
Prader-Willi/Angelman syndrome, 75, 86, 103–104ge
Precancer, 31–34, 46–47, 49–51ge, 53ge, 55–56ge, 58–59ge,
87–88, 203, 338
regression, 58ge
Precancerous condition, 31, 58ge
Precision
data, 5, 263–318
diagnosis, 106ge, 215–216, 247–258, 338
medicine, 1–13, 17, 69–109, 117, 153–154, 160–162, 187,
234, 270, 285–298, 301, 327–358
taxonomy, 210–223
Preclinical trial, 352–353, 358ge
Precursor lesions, 32, 44, 248
Precursors, 32–34, 44, 89, 95, 106ge, 134, 248, 252ge,
254–255ge
Predictive analytics, 257ge, 357ge
Predictive test, 10ge, 257ge
Predictor, 257ge
Premalignancy, 59ge
Premature aging disorders, 71
Premature aging syndrome, 70, 101ge
Prevalence, 11–12ge, 24, 27, 59ge, 101ge, 131, 154, 358ge
Prevotella dentalis, 213
Primary biliary cirrhosis, 197
Primary cilia, 9ge, 123
Primary data, 307, 315ge
Primary disease, 6, 9ge, 11–12ge, 92
vs. secondary disease, 10ge
Primary effusion lymphoma, 45
Primary erythromelalgia, 10ge, 124–125
Primary host, 220ge, 223ge
Prion, 144ge, 181, 183, 187, 203, 214, 221ge, 354
Prion disease, 119, 126, 144ge, 354
Privacy vs. confidentiality, 307, 315ge
Prognosis, 2, 10ge, 95, 108ge, 250–252
Progressive neurodegenerative disorder, 71–72
Prokaryotes, 57ge, 183, 191, 213
Promoter, 46–47, 52ge, 56ge, 59ge, 100–101ge, 106ge, 219ge
Promyelocytic leukemia, 87, 161
Properties vs. classes, 257ge
Prospective clinical trial, 344, 349
Protein folding disorders, 199
Protein misfolding disorders, 198
Proto-oncogenes, 46–47, 48ge, 57ge, 59ge, 222ge
Protozoa, 59ge, 205, 213, 216–217
Protozoal infections, 258ge
Protozoans, 23, 59ge, 205, 216–217, 258ge
Proviruses, 46
Proximate cause, 13ge, 37–38, 40, 59ge
Prurigo nodularis, 207
Psammoma bodies, 126, 279
PSA value, 277
PSEN1 gene, 95
Pseudallescheria boydii, 216
Pseudallescheriosis, 216
Pseudoagouti, 86–87
Pseudoconvergent diseases, 124
Pseudodichotomy, 199, 243
Pseudogene, 85, 106ge
Pseudo-Pelger-Huet, 128
Psoriasis, 139
Psychiatric disorder, 7
PTEN gene, 102ge, 156
Public Law, 11ge, 154, 346
Pulmonary alveolar proteinosis (PAPs), 126, 133,
137–138, 346
Putative causal oncogene, 28
P value, 314ge, 358ge
Pyoderma gangrenosum, 54ge, 198, 220ge
Pyogenic arthritis, 54ge, 198, 220ge
Pyogenic arthritis, pyoderma gangrenosum, and acne
(PAPA), 198
Pyruvate dehydrogenase, 100ge

Q
Quantitative traits, 136, 334
Quaternary disease, 12ge

R
Rag1 and Rag2 recombinase, 80
Ragged red fiber myopathy, 279
Rag genes, 192–193, 218ge
Randomized control trial, 343, 348–349
Randomly occurring mutation, 98ge
Random number, 266, 296–298
Rare diseases, 4, 19, 73, 122, 153–176, 204, 248, 289, 330
  Act of 2002, 11ge, 154
Recessive polycystic kidney disease, 122–123
Recommender algorithms, 243–244, 258ge
Redundant systems, 197
Reference laboratories, 213–214
Reflection, 30, 270, 313ge
Refractory anemia, 97, 104ge
Refsum disease, 222ge
Regression, 33–34, 47, 50ge, 55–56ge, 58–59ge
Regulatory element, 9ge, 42–43, 61ge, 76, 106ge, 145ge
Reidentified record, 312ge
Relapsing polychondritis, 197
Renal angiomyolipoma, 83, 102ge, 174ge
Renal artery dysplasia, 136f
Renal cell carcinoma (RCCs), 83, 106ge, 242–243, 282–283
Renal tubular acidosis, 128
Repeatability, 26, 343
Retinal dystrophy, 120, 122–123
Retinitis, 73
pigmentosa, 8ge, 70, 73, 101ge, 107ge, 119–120, 123,
142ge, 173ge
Retrospective data, 349
Retrovirus, 46–47, 57ge, 59ge, 182, 190, 192–193, 199,
221–223ge, 339
Rett syndrome, 7, 90, 100ge
Rhabdoid tumor, 47, 60ge, 88–90, 89f, 354
Rhabdoid tumor A, 60ge
Rhabdomyosarcoma, 49ge, 103ge
Rheumatic fever, 21–22, 132–133, 203, 233–234
Rheumatoid arthritis, 131–132, 134, 139, 197, 201, 346,
349–350
Rh incompatibility disease, 131
Rickettsia, 55ge, 191, 217, 221ge
Rickettsiosis, 217
Rieger syndrome, 53ge
Risk prediction, 248, 251–252
RNA polymerase, 59ge, 106ge, 219ge
RNA silencing, 101ge
RNA splicing, 85, 98ge
RNA that might influence gene, 85
Roberts syndrome, 83, 128, 355ge
Rochalimaea quintana, 217
Rod cone dystrophy, 72
Root cause, 2, 9–10ge, 12–13ge, 24–25, 46, 51ge, 56ge, 60ge,
71, 76–79, 81, 85, 89–90, 92, 96, 117, 121, 125, 127,
129, 137, 140, 144ge, 154–155t, 158–160, 188–189,
192–193, 195, 207, 303, 341, 345, 347–348, 351–352,
355ge
Rubber and tire manufacturing industries, 166
Russell-Silver syndrome, 75, 86, 106ge

S
Sample contamination, 216, 300
Sample size, 106ge, 296, 298, 315ge
Sampling errors, 278, 300
Sampling size, 298
SAPHO syndrome (synovitis, acne, pustulosis,
  hyperostosis, and osteitis), 207
Sarcoglycan complex synthesis, 164
Sarcoglycanopathies, 164
Sarcoidosis, 54ge, 207, 220ge
Sars virus, 219ge
Schnitzler syndrome, 199
Schwannoma, 56ge, 138, 145ge
SCID. See Severe combined immunodeficiency disease
(SCID)
SCN5A gene, 95
Screening tests, 248, 257ge, 338
Scurvy, 128, 233–234
Secondary data, 11ge, 315–316ge
Secondary disease, 6, 9–12ge
Secondary HLH, 199
Secretory breast carcinoma, 250
Self-limited disease, 210
Seminoma, 156–157, 161, 175ge, 238, 255ge, 339
Senior-Loken syndrome, 122–123
Serotype, 215, 223ge
Serratia marcescens, 212
Severe combined immunodeficiency disease (SCID), 46,
72, 80, 84
SGCD gene, 95
Shagreen patches, 54ge
Shoe industry workers, 166
Short rib polydactyly, 122–123
Short stature homeobox, 53ge
Shwachman-Diamond syndrome, 24–25, 95–96
Sick building syndrome, 207
Sickle cell anemia, 119, 334, 354ge
Sickle cell disease, 50ge, 56ge, 107ge, 119, 143ge, 160, 195,
334–335, 354ge
Sideroblastic anemia, 97, 104ge, 128
Signaling pathway, 139, 170
Sign of Leser-Trelat, 144ge
Silent mutation, 106ge
Similarity scores, 282–283
Single gene disease, 70–71, 96, 105ge, 107ge
Single nucleotide polymorphism (SNP), 56ge, 78–79,
98ge, 102ge, 106–107ge, 174ge, 336
  in GWAS, 174ge
Single nucleotide variant (SNV), 106ge
Sister chromatid cohesion, 83
Situs inversus, 105ge, 124f
Sjogren’s syndrome, 207
Sleeping sickness, 187–188
Small cell carcinoma, 173ge
Small interfering RNA, 106ge
Smallpox virus, 172
Smokovia, 18–19
Smoothened, a multifuctional protein, 170
Sodium channel, 10ge, 124–125, 142ge, 165
Soma, 34, 60ge
Somatic
  mosaicism, 103ge, 107ge, 335
  mutation, 33–34, 56ge, 59–60ge, 75, 98ge, 107ge, 165,
  335–336, 357ge
Soot, 166
Speciation, 35, 105ge, 258ge
Species, 2, 8–10ge, 17, 21–22, 31–33, 35, 41–42, 49ge, 53ge,
60ge, 75–76, 104–106ge, 119–120, 142–144ge,
171–172, 181–184, 187–188, 188–189t, 190–191,
193–194, 196, 202–204, 210–211, 213–217,
219–221ge, 223ge, 230–231, 240–242, 248–249,
253ge, 255ge, 257ge, 280f, 284, 287–288, 332, 334,
351–354, 356ge, 358ge
Spherocytosis, 70
Spliceosome disease, 107ge
Spliceosome disorder, 98ge
Spondyloarthropathies, 199
Spontaneous abortions, 128, 336
Spontaneous cerebrospinal fluid leak, 207
Spontaneously regressing diseases, 59ge
Spontaneous regression, 33–34, 55ge
Spontaneous vs. sporadic, 60ge
Sporadic, 12ge, 24–25, 50ge, 60ge, 94–96, 119–120, 126, 171,
355ge
prion disease, 119
retinoblastomas, 313ge
Sporadic disease, 6, 24–25, 119, 293
  vs. phenocopy disease, 145ge
Sporozoite, 216–217
Squamous carcinoma, 33, 35, 45, 54ge, 107ge, 166, 173ge,
203
Src oncogene, 46–47
Staging, 245–246, 250–252, 258ge
Statin, 4, 171–172
Statistical anomaly, 246–247
Stem cells, 30, 49ge, 52ge, 56ge, 58ge, 60–61ge, 101ge, 105ge,
107–108ge, 166
Stickler syndrome, 70
Stiff person syndrome, 207
Stoichiometric, 40–41
Stroke, 158, 182
Subclassify, 230–231, 245
Subpopulation of cells, 59ge, 73, 88, 238, 296
Subtypes of disease, 98ge, 250, 278, 292, 348
Succinate dehydrogenase (SDHB), 80–81
Sudden infant death syndrome (SIDS), 287
Sudden unexpected death syndrome, 207
SUMO, 256ge
Superclass, 6, 8ge, 10ge, 12ge, 222ge, 232–239, 257ge, 286,
311ge
Susceptibility, 2, 7ge, 12ge, 50ge, 73, 77–78, 91, 144ge, 157,
190, 195, 212, 222ge, 251, 332–333, 351
Susceptibility gene, 12ge, 77–78, 294
SV40-like polyoma virus, 339
SV40 virus, 339
Sweating sickness, 207
Syncytin, 193
Syncytiotrophoblasts, 238
Synovial osteochondromatosis, 207
Systemic lupus erythematosus, 133–134, 197, 251, 346,
349–350

T
Taino, 189, 196
Takayasu’s arteritis, 207
Tangier disease, 127, 164
Targeted therapies, 94, 245
Tau encephalopathy, 128
Tau protein, 281, 354ge
Taxon-A, 223ge
Taxonomy, 6, 12ge, 97–98, 181–189
T cells, 46, 72, 80, 131, 161–162, 192–193, 197, 218ge, 339
Telomere, 354
Teratomas, 30, 238, 255ge
Teratomatous neoplasms, 235
Tertiary disease, 11–12ge
Thalamus, 159
Thalidomide, 128, 168
Thesaurus, 223ge
Thorotrast, 166–167
Thrombocythemia, 56ge, 98ge, 105ge, 139, 165
Thyroid carcinoma, 12ge, 169, 181–189, 210–223, 232,
240–241, 253–254ge
Ticks, 202, 223ge
Tietz syndrome, 81–82
Time stamp, 264, 266–269, 271–272, 313ge, 317ge
Timothy syndrome, 142ge
Tnf receptor associated periodic syndrome, 220ge
TNNC1 gene, 95
TNNT2 gene, 95
Torticollis, 138, 171, 207, 350
Totipotent stem cell, 56ge, 58ge, 61ge, 90–91, 235, 238,
255ge
Toxin, 9ge, 18, 20, 22–23, 29–30, 44, 94, 101ge, 105ge,
129–130, 138, 143ge, 156–157, 159, 165–166,
168–169, 171, 194–196, 207, 235, 281, 350, 353–354
Toxoplasmosis, 184, 194–195, 339
TP53 gene, 71, 73
Trachoma, 208
Trans acting, 101ge, 108ge
Transcription factor, 9ge, 42–43, 53ge, 58–59ge, 61ge, 76,
82, 101–102ge, 106ge, 118, 145ge
Transdifferentiation, 108ge
Transfected T cells, 162
Transgenic strain, 47, 76
Translational research, 141, 145ge
Translation factor, 145ge
Translocation, 48ge, 57–58ge, 87, 108–109ge, 222ge, 251
Transplacental carcinogenesis, 167, 175ge
Transposable element, 223ge
Transposon, 106ge, 190, 221ge, 223ge
Trastuzumab, 348
Trench fever, 202
Trichodynia, 207
Trigger finger, 207
Trilateral retinoblastoma, 318ge
Trinucleotide repeat disorder, 71–72, 108ge
Triple, 42, 254ge, 267, 316ge
Trophoblastic tumor, 238, 255ge
Trophozoite, 216–217
Tropical sprue, 207
Trypanosoma brucei, 187–188
TSC2 gene, 139, 174ge
Tuberculosis, 22, 27, 182, 189, 194–196, 339, 351
Tuberous sclerosis (TSC), 27, 54ge, 72, 95, 102–103ge,
138–139, 174ge, 354, 354ge
Tubers, 102ge, 138, 354
Tumor necrosis factor receptor-associated periodic
syndrome (TRAPS), 199
Tumor speciation, 35
Turner syndrome, 53ge
Type 2 diabetes mellitus, 4, 11ge, 76, 121, 157, 199, 334
Type errors, 318ge
Type II pneumocytes, 137
Tyrosine kinase, 58ge, 140–141, 161

U
Ubiquitin protein, 61ge
Ulcerative colitis, 293–294
Ultraviolet light, 77–78, 158
Unclassifiable objects, 258ge
Undiagnosed JAK2 mutation, 165
Undifferentiated tumors, 108ge
Unikonta, 186, 241–242
Uniparental disomy, 54ge, 98ge, 109ge
Uniqueness, 2, 13ge, 264–265, 272, 275, 337
Universal and perpetual, 318ge
Universally unique identifier (UUID), 265–266, 318ge
Unphagocytosed organisms, 281
Ureteric bud, 242–243
Usher Syndrome, 70, 101ge, 122–123, 175ge
U.S. Navy, 212

V
Validation, 5–7, 13ge, 125, 301–302, 316ge, 342–343,
348–349
Vanishing bone disease, 206
Vanishing white matter, 145ge
Variable expressivity, 74–76, 109ge
Vascular endothelial growth factor, 120
Vascular malformation, 99ge
Vector, 162, 202, 219ge, 223ge
VEGFC, 120
Velocardiofacial syndrome, 104ge
Ventilator tubes, 214
Verrucous carcinoma, 339
Vesicular trafficking disorders, 164, 173ge, 175ge
Vesicular transport disorder, 175ge, 281, 354
Visceral situs anomalies, 122–123
Vital signs, 44, 328–329
Vitamin B, 97, 128
V(D)J recombination units, 80
Von Hippel-Lindau disease, 109ge
Von Recklinghausen disease, 56ge, 109ge
Von Willebrand disease, 127, 135

W
Waardenburg syndrome, 53ge, 61ge, 81–82, 145ge
Warfarin embryopathy, 121–122, 128
Warthin tumor, 159, 176ge
Wegener’s syndrome, 206
Wernicke-Korsakoff syndrome, 159
West nile fever virus, 219ge
WHIM, 76
Whipple disease, 203–204, 208, 281, 356ge
Wild-type gene, 145ge
Wilms tumors, 49ge, 53ge, 73, 243
Wilson disease, 128
Wolbachia pipientis, 208, 219ge
Wolf-Hirschhorn syndrome, 53ge, 104ge
Woolly hair, 70
World Health Organization, 13ge, 182, 216, 234, 337
Wrong statistical test, 318ge

X
X chromosome, 7, 85–86, 109ge, 279, 318ge, 336
Xeroderma pigmentosum, 109ge, 354
X-linked lymphoproliferative syndrome, 199
XML (eXtended Markup Language), 264–265, 317ge

Y
Y chromosome, 102ge, 109ge, 308, 318ge, 354ge
Yellow fever virus, 219ge, 223ge

Z
Zebrafish, 195, 347–348, 351
Zika virus, 188t
Zipf distribution, 291, 292f, 314–315ge, 358ge
Zollinger-Ellison syndrome, 164–165t
Zoonosis, 5, 26, 342
Zygote, 3, 35, 51–52ge, 54ge, 85, 107ge, 335–336, 356ge

Announcement: Precision Medicine and the Reinvention of Human Disease 22 Jan 2018 4:55 AM (7 years ago)

In January, 2018, Academic Press is publishing my latest book, Precision Medicine and the Reinvention of Human Disease

Here is the book description, from the back cover:

Despite what you may have read in the popular press and in social media, Precision Medicine is not devoted to finding unique treatments for individuals, based on analyzing their DNA. To the contrary, the goal of Precision Medicine is to find general treatments that are highly effective for large numbers of individuals who fall into precisely diagnosed groups.

We now know that every disease develops over time, through a sequence of defined biological steps, and that these steps may differ among individuals, based on genetic and environmental conditions. We are currently developing rational therapies and preventive measures, based on our precise understanding of the steps leading to the clinical expression of diseases.

Precision Medicine and the Reinvention of Human Disease explains the scientific breakthroughs that have changed the way that we understand diseases, and reveals how medical scientists are using this new knowledge to launch a medical revolution.

Key Features

• Clarifies the foundational concepts of Precision Medicine, distinguishing this field from its predecessors such as genomics, pharmacogenetics, and personalized medicine.
• Gathers the chief conceptual advances in the fields of genetics, pathology, and bioinformatics, and synthesizes a coherent narrative for the field of Precision Medicine.
• Delivers its message in plain language, and in a relaxed, conversational writing style, making it easy to understand the complex subject matter.
• Guides the reader through a coherent and logical narrative, gradually providing expertise and skills along the way.
• Covers the importance of data sharing in Precision Medicine, and the many data-related challenges that confront this fragile new field.

Table of Contents

Preface.                                                                 

Chapter 1. Introduction: Seriously, What is Precision Medicine?                     
  Glossary
  References


Chapter 2. Redefining Disease Causality                                            
  Section 2.1 Causality and Its Paradoxes                              
  Section 2.2 Why We Are Confident that Diseases Develop in Steps                  
  Section 2.3 Cause of Death                                           
  Section 2.4 What Is a Disease Pathway?                                              
  Section 2.5 Does Single Event Pathogenesis Ever Happen?      
  Glossary
  References


Chapter 3. Genetics: Clues, Not Answers, to the Mysteries of Precision Medicine         
  Section 3.1 Inscrutable Genes                                               
  Section 3.2 Inscrutable Diseases                                            
  Section 3.3 Recursive Epigenomic/Genomic Diseases                                              
  Section 3.4 Why a Gene-based Disease Classification Is a Bad Idea           
  Glossary
  References


Chapter 4. Disease Convergence
  Section 4.1 Mechanisms of Convergence                                       
  Section 4.2 Phenocopy Diseases: Convergence Without Mutation        
  Section 4.3 The Autoantibody Phenocopies                                      
  Section 4.4 Pathway-Directed Treatments for Convergent Diseases             
  Glossary
  References


Chapter 5. The Precision of the Rare Diseases                                          
  Section 5.1 The Biological Differences Between Rare Diseases and Common Diseases   
  Section 5.2 Precision Medicine's First Benefit: Cures for Rare Diseases            
  Section 5.3 What the Rare Diseases Tell Us About the Common Diseases               
  Section 5.4 Treatments for Rare Diseases are Effective Against the Common Diseases 
  Glossary
  References


Chapter 6. Precision Organisms                                                        
  Section 6.1 Modern Taxonomy of Infectious Diseases                                    
  Section 6.2 Our Genome Is a Book Titled "The History of Human Infections"           
  Section 6.3 Inflammatory Diseases: Collateral Damage in the War on Human Infection    
  Section 6.4 Revising Koch's Postulates in the Era of Precision Diagnostics      
  Section 6.5 Diseases-in-waiting                                              
  Section 6.6 Precision Taxonomy                                               
  Glossary
  References


Chapter 7. Reinventing Diagnosis                                                  
  Section 7.1 The Principles of Classification               
  Section 7.2 Superclasses                                                     
  Section 7.3 Classifications Cannot Be Based on Similarities                       
  Section 7.4 The Horrible Consequences of Class Blending                
  Section 7.5 What Is Precision Diagnosis?                                   
  Glossary
  References


Chapter 8. Precision Data                                                              
  Section 8.1 What Are the Minimal Necessary Properties of Good Data?              
  Section 8.2 Data Identification and Data Deidentification                      
  Section 8.3 What Do We Do With Non-quantitative, Descriptive Data?    
  Section 8.4 Incredibly Simple Methods to Understand Precision Medicine Data         
  Section 8.5 Data Reanalysis: More important than the Original Data Analysis      
  Section 8.6 What Is Data Sharing, and Why Don't We Do More of It?              
  Glossary
  References


Chapter 9. The Alternate Futures of Precision Medicine                               
  Section 9.1 Hypersurveillance                           
  Section 9.2 Do It Yourself Medicine                                   
  Section 9.3 Eugenics                                          
  Section 9.4 Public Health                                                
  Section 9.5 The Data Analyst of Tomorrow                           
  Section 9.6 Fast, Cheap, Precise Clinical Trials              
  Section 9.7 Animal Experimentation                 
  Glossary
  References