Sintchenko Infectious Disease Informatics

1. Auflage 2009
ISBN: 978-1-4419-1327-2
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 434 Seiten

There are several reasons to be interested in infectious disease informatics. First, it is of practical significance to understand how the technology revolution has been reshaping infectious disease research and management, as rapid advances in geno- associated technologies have changed the very nature of the questions we can ask. Second, the emerging evidence has confirmed that the application of information technologies in healthcare enhances our ability to deal with infectious diseases. Finally, the implementation of electronic health records has created new and exciting opportunities for secure, reliable and ethically sound clinical decision support and biosurveillance guided by the genomics of pathogens with epidemic potential. This volume addresses the growing need for the critical overview of recent developments in microbial genomics and biomedical informatics relevant to the control of infectious diseases. This field is rapidly expanding, and attracts a wide audience of clinicians, public health professionals, biomedical researchers and computer scientists who are fascinated by the complex puzzle of infectious disease. This book takes a multidisciplinary approach with a calculated move away from the traditional health informatics topics of computerized protocols for antibiotic p- scribing and pathology testing. Instead authors invite you to explore the emerging frontiers of bioinformatics-guided pathogen profiling, the system microbiolo- enabled intelligent design of new drugs and vaccines, and new ways of real-time biosurveillance and hospital infection control. Throughout the book, references are made to different products supplied by public sources and commercial vendors, but this is not an endorsement of these products or vendors.

Vitali Sintchenko (MBBS, PhD, FRCPA, FACHI) is a clinical microbiologist and informatician with the Sydney Medical School, The University of Sydney, Australia.

Autoren/Hrsg.

Sintchenko, Vitali

Inhaltsverzeichnis

1;Sintchenko_FM_O.pdf;1
1.1;Anchor 1;7
2;Sintchenko_Ch01_O.pdf;11
2.1;Chapter 1;11
2.1.1;Informatics for Infectious Disease Research and Control;11
2.1.1.1;1.1 Introduction;11
2.1.1.2;1.2 Handling New Data Types;12
2.1.1.2.1;1.2.1 Microbial Genome Assembly and Annotation;12
2.1.1.2.2;1.2.2 Meta-Omics: Metagenomics and Metaproteomics;15
2.1.1.2.3;1.2.3 Global Genome Analysis;17
2.1.1.3;1.3 Changing the Way Discoveries Are Made;18
2.1.1.3.1;1.3.1 Knowledge Discovery from Comparative Genomics;18
2.1.1.3.2;1.3.2 Automatic Recognition of Functional Regions;19
2.1.1.3.3;1.3.3 Enabling the Dynamic View of Infectious Diseases;20
2.1.1.3.4;1.3.4 Cross-Validating the Knowledge Sources;22
2.1.1.4;1.4 Enabling Knowledge Communities: eScience;23
2.1.1.4.1;1.4.1 Novel Infrastructures Support Knowledge Communities;23
2.1.1.4.2;1.4.2 Data Aggregation;24
2.1.1.5;1.5 Translating “Omics” into Clinical Practice;25
2.1.1.5.1;1.5.1 Rapid Identification of Pathogens;25
2.1.1.5.2;1.5.2 Guiding Antibiotic Prescribing Decisions;26
2.1.1.5.3;1.5.3 Linking Genomics to Clinical Outcomes;27
2.1.1.5.4;1.5.4 Tracing Pathogens with Epidemic Potential;28
2.1.1.6;1.6 Conclusions;30
2.1.2;References;31
3;Sintchenko_Ch02_O.pdf;37
3.1;Chapter 2;37
3.1.1;Bioinformatics of Microbial Sequences;37
3.1.1.1;2.1 Overview of Prokaryotic Microorganisms;37
3.1.1.1.1;2.1.1 Nature of the Bacterial Genome;37
3.1.1.1.2;2.1.2 Bacterial Evolution and the Universal Tree;38
3.1.1.2;2.2 Classification of Prokaryotic Microorganisms;41
3.1.1.3;2.3 Revealing Phylogenies and Population Structures;42
3.1.1.3.1;2.3.1 Methods for Revealing the Extent and Frequency of LGT;42
3.1.1.3.2;2.3.2 Methods for Depicting Population Structures and Phylogenies;44
3.1.1.3.3;2.3.3 Comparisons of Entire Genomes;50
3.1.1.4;2.4 Impact of Advances in Microbial Evolution on the Practice of Microbiology;50
3.1.1.4.1;2.4.1 Bacillus anthracis;51
3.1.1.4.2;2.4.2 Staphylococcus aureus;53
3.1.1.4.3;2.4.3 Campylobacter jejuni and;55
3.1.1.4.4;2.4.4 Streptococcus agalactiae;56
3.1.1.5;2.5 Concluding Remarks;58
3.1.2;References;58
4;Sintchenko_Ch03_O.pdf;63
4.1;Chapter 3;63
4.1.1;Mining Databases for Microbial Gene Sequences;63
4.1.1.1;3.1 Introduction;63
4.1.1.2;3.2 Retrieval of Target Sequences;66
4.1.1.2.1;3.2.1 Retrieval by Similarity;66
4.1.1.2.2;3.2.2 Retrieval by Keywords;66
4.1.1.2.3;3.2.3 The Brute Force Approach: By Keywords;69
4.1.1.2.4;3.2.4 The Brute Force Approach: By Similarity;70
4.1.1.3;3.3 Retrieval of Published Primers;71
4.1.1.3.1;3.3.1 A Note of Caution About PubMed Queries;71
4.1.1.3.2;3.3.2 Primer Extraction;73
4.1.1.4;3.4 Assessing Primers;75
4.1.1.5;3.5 Concluding Remarks;78
4.1.2;References;80
5;Sintchenko_Ch04_O.pdf;82
5.1;Chapter 4;82
5.1.1;Comparative Genomics of Pathogens;82
5.1.1.1;4.1 Introduction;82
5.1.1.2;4.2 Tools for Microbial Classification and Identification of Pathogens;83
5.1.1.2.1;4.2.1 Sequencing of Selected Genes and Genomes;85
5.1.1.2.2;4.2.2 DNA Hybridization-Based Approaches;87
5.1.1.2.3;4.2.3 Polymerase Chain Reaction (PCR)-Based Approaches;90
5.1.1.2.4;4.2.4 Pyrosequencing-Based Approaches;91
5.1.1.3;4.3 Metagenomics: Principles and Perspectives;92
5.1.1.4;4.4 Emerging DNA Sequencing Technologies;93
5.1.1.5;4.5 Conclusions;96
5.1.2;References;97
6;Sintchenko_Ch05_O.pdf;101
6.1;Chapter 5;101
6.1.1;Systems Microbiology: Gaining Insights in Transcriptional Networks;101
6.1.1.1;5.1 Systems Microbiology: Introduction;101
6.1.1.2;5.2 High–Throughput Data Sources;103
6.1.1.2.1;5.2.1 Expression Data;103
6.1.1.2.2;5.2.2 Regulator-Target Interaction Data;104
6.1.1.3;5.3 Reconstruction of Transcriptional Networks;105
6.1.1.3.1;5.3.1 Reconstructing from “Omics” Data;105
6.1.1.3.2;5.3.2 Benchmarking Algorithms;106
6.1.1.3.3;5.3.3 Which Method to Choose for Network Reconstruction?;107
6.1.1.3.3.1;Box 5.1 Overview of network inference methods;109
6.1.1.3.4;5.3.4 Module Inference: Learning About Co-Expressed Targets;113
6.1.1.3.4.1;5.3.4.1 From Clustering to Biclustering;113
6.1.1.3.4.2;5.3.4.2 Global vs. Query-Driven Biclustering;114
6.1.1.3.4.2.1;Integrative Biclustering: From Co-Expression Towards Co-Regulation;114
6.1.1.3.5;5.3.5 Inference of the Regulatory Program;115
6.1.1.3.5.1;5.3.5.1 Regulatory Program Inference from Microarray Data Only vs. Data-Integration;116
6.1.1.3.5.2;5.3.5.2 Module-Based vs. Direct Network Inference;117
6.1.1.3.5.3;5.3.5.3 Supervised vs. Unsupervised Inference of the Regulatory Program;118
6.1.1.3.6;5.3.6 Data Integration;119
6.1.1.3.7;5.3.7 Prioritization of Predictions;120
6.1.1.4;5.4 High-Throughput Data Can Assist in the Search for Novel Drug and Vaccine Targets;121
6.1.1.4.1;5.4.1 5.4.1 Revealing the Mechanisms of Action;121
6.1.1.4.2;5.4.2 The Search for Novel Targets;121
6.1.1.5;5.5 Conclusions and Perspectives;124
6.1.2;References;126
7;Sintchenko_Ch06_O.pdf;131
7.1;Chapter 6;131
7.1.1;Host–Pathogen Systems Biology;131
7.1.1.1;6.1 Introduction;131
7.1.1.2;6.2 Systems Biology in Drug Discovery;133
7.1.1.3;6.3 Computational Systems Biology Models, Methods and Tools;136
7.1.1.3.1;6.3.1 Scales and Models;136
7.1.1.3.2;6.3.2 Methods;136
7.1.1.3.3;6.3.3 Static Networks;138
7.1.1.3.4;6.3.4 Response Networks;138
7.1.1.3.5;6.3.5 Modeling Techniques;139
7.1.1.4;6.4 Intracellular Models;139
7.1.1.4.1;6.4.1 Genomic Foundation of Host-Pathogen Interactions;140
7.1.1.4.2;6.4.2 Large-Scale Host Response Models;142
7.1.1.4.3;6.4.3 Immune-Receptor Signaling;142
7.1.1.5;6.5 Intercellular or Cell Host-Pathogen Interaction Models;145
7.1.1.6;6.6 Large Scale Models of Host–Pathogen Physiology;147
7.1.1.7;6.7 Conclusion;150
7.1.1.7.1;Anchor 48;132
7.1.1.7.1.1;Box 6.1 Immune system overview;132
7.1.2;References;152
8;Sintchenko_Ch07_O.pdf;156
8.1;Chapter 7;156
8.1.1;Text Mining for Discovery of Host–Pathogen Interactions;156
8.1.1.1;7.1 Introduction;156
8.1.1.2;7.2 Corpus Construction;157
8.1.1.3;7.3 Biomedical Corpora;158
8.1.1.4;7.4 Named Entity Recognition;159
8.1.1.5;7.5 Syntactic Parsing;160
8.1.1.6;7.6 Relationship Extraction;160
8.1.1.7;7.7 Case Study: Pathogen–Host Relationship Extraction;161
8.1.1.7.1;7.7.1 Gene and Genotype Recognition;162
8.1.1.7.2;7.7.2 Pathogen Recognition;164
8.1.1.7.3;7.7.3 Disease and Syndrome Recognition;165
8.1.1.7.4;7.7.4 Association Mining;165
8.1.1.7.5;7.7.5 Potential Directions for Relationship Extraction;167
8.1.1.8;7.8 Concluding Remarks;169
8.1.2;References;170
9;Sintchenko_Ch08_O.pdf;173
9.1;Chapter 8;173
9.1.1;A Network Approach to Understanding Pathogen Population Structure;173
9.1.1.1;8.1 Introduction;173
9.1.1.2;8.2 Contact Networks and Disease Transmission;175
9.1.1.2.1;8.2.1 Sexually Transmitted Diseases and Host Contact Networks;175
9.1.1.2.2;8.2.2 Directly Transmitted Diseases and Host Contact Networks;176
9.1.1.3;8.3 Host Contact Networks and Pathogen Evolution;177
9.1.1.3.1;8.3.1 Evolution of Pathogen Traits and Host Contact Networks;177
9.1.1.3.2;8.3.2 Pathogen Population Structure;178
9.1.1.3.3;8.3.3 Community Structure in Host Networks and Pathogen Population Structure;181
9.1.1.4;8.4 Antigen Networks;184
9.1.1.4.1;8.4.1 Malaria Antigen Networks;184
9.1.1.4.2;8.4.2 Conceptual Antigen Networks and Influenza Dynamics;186
9.1.1.5;8.5 Conclusion;187
9.1.2;References;190
10;Sintchenko_Ch09_O.pdf;192
10.1;Chapter 9;192
10.1.1;Computational Epitope Mapping;192
10.1.1.1;9.1 Introduction;192
10.1.1.2;9.2 The Principal Molecular Varieties of Epitope;194
10.1.1.3;9.3 T-cell and B-cell Epitope Prediction In Silico;198
10.1.1.4;9.4 Conclusion;204
10.1.2;References;204
11;Sintchenko_Ch10_O.pdf;208
11.1;Chapter 10;208
11.1.1;Pangenomic Reverse Vaccinology;208
11.1.1.1;10.1 Introduction;208
11.1.1.2;10.2 Single Genome Analysis;210
11.1.1.2.1;10.2.1 The Annotation Procedure;210
11.1.1.2.2;10.2.2 Review of the Methods for Protein Localization Prediction;212
11.1.1.3;10.3 Pangenomic Analysis;214
11.1.1.3.1;10.3.1 Methods for Ortholog Identification;214
11.1.1.3.2;10.3.2 Allelic Variation in Candidate Antigens;216
11.1.1.4;10.4 Experimental Validation;217
11.1.1.4.1;10.4.1 Experimental Validation Procedure;217
11.1.1.4.2;10.4.2 Reverse Vaccinology Case Studies;218
11.1.1.5;10.5 Bacterial Population Genetics and Vaccine Design;219
11.1.1.5.1;10.5.1 Genetic Variability Between Subpopulations;220
11.1.1.5.2;10.5.2 Vaccine-Oriented Antigenic Typing;222
11.1.1.6;10.6 Conclusion;222
11.1.2;References;223
12;Sintchenko_Ch11_O.pdf;227
12.1;Chapter 11;227
12.1.1;Immunoinformatics: The Next Step in Vaccine Design;227
12.1.1.1;11.1 Introduction;227
12.1.1.2;11.2 Technological Advances;229
12.1.1.2.1;11.2.1 Immunoinformatics for Vaccine Design;229
12.1.1.2.2;11.2.2 Improved Delivery Vehicles;231
12.1.1.2.2.1;11.2.2.1 Targeting Dendritic Cells;232
12.1.1.2.2.2;11.2.2.2 Mucosal Delivery;234
12.1.1.2.2.3;11.2.2.3 Improved Adjuvants;235
12.1.1.2.2.4;11.2.2.4 Multi-functional T Cells;236
12.1.1.3;11.3 Advantages and Disadvantages of T-cell Directed Vaccines;236
12.1.1.4;11.4 Examples of T-cell Epitope-Driven Vaccines;238
12.1.1.4.1;11.4.1 TulyVax;238
12.1.1.4.2;11.4.2 HelicoVax;239
12.1.1.4.3;11.4.3 VennVax;240
12.1.1.5;11.5 Concluding Remarks;242
12.1.2;References;243
13;Sintchenko_Ch12_O.pdf;249
13.1;Chapter 12;249
13.1.1;Understanding the Shared Bacterial Genome;249
13.1.1.1;12.1 Introduction;249
13.1.1.2;12.2 Ecological Niche and Adaptive Capacity;250
13.1.1.3;12.3 The Shared Genome;253
13.1.1.3.1;12.3.1 Gene Capture and Transfer;254
13.1.1.3.2;12.3.2 Associations Between R Genes and ME;254
13.1.1.3.3;12.3.3 b-Lactamases Conferring Resistance to Cephalosporins;255
13.1.1.3.4;12.3.4 Genetic Disequilibrium Within the Mobile Gene Pool; the Multi-(Antibiotic) Resistance Region;256
13.1.1.3.5;12.3.5 The Arrival and Spread of New Members of the Gene Pool;257
13.1.1.3.6;12.3.6 Comparative Analysis of Multiresistance Regions;258
13.1.1.3.7;12.3.7 Conjugative Plasmids: The Need for a New Metagenomics Strategy;260
13.1.1.4;12.4 Concluding Remarks;262
13.1.2;References;263
14;Sintchenko_Ch13_O.pdf;266
14.1;Chapter 13;266
14.1.1;Computational Grammars for Interrogation of Genomes;266
14.1.1.1;13.1 Introduction;266
14.1.1.1.1;Box 13.1 List of common mobile genetic elements (MGEs) associated with antibiotic resistance. For a more detailed introduction;267
14.1.1.2;13.2 Automatic Annotation of Bacterial DNA;267
14.1.1.3;13.3 Computational Grammars;268
14.1.1.4;13.4 Annotating Biological Structure Using Grammar Models;270
14.1.1.4.1;13.4.1 DNA Tokenization;271
14.1.1.4.2;13.4.2 Grammar Class and Parsing Algorithm;272
14.1.1.4.3;13.4.3 Grammar Derivation;273
14.1.1.4.4;13.4.4 Validation of Grammatical Models;274
14.1.1.5;13.5 Case Study: A Grammar Model for Cassette Array Modeling and Interrogation;274
14.1.1.5.1;13.5.1 DNA Tokenization;275
14.1.1.5.2;13.5.2 Cassette Array Grammar;275
14.1.1.6;13.6 Interrogation of Annotated Structures;275
14.1.1.6.1;13.6.1 Indexing Hierarchical Genetic Structures;276
14.1.1.6.2;13.6.2 A Query Language for Structure Annotations;276
14.1.1.6.3;13.6.3 Structure Visualization;277
14.1.1.7;13.7 Conclusion;278
14.1.2;References;279
15;Sintchenko_Ch14_O.pdf;282
15.1;Chapter 14;282
15.1.1;In silico Discovery of Chemotherapeutic Agents;282
15.1.1.1;14.1 Introduction;282
15.1.1.2;14.2 In Silico Identification and Selection of Chemotherapeutic Target Candidates;284
15.1.1.2.1;14.2.1 Target Discovery Overlapping with In Silico Drug Discovery;284
15.1.1.2.2;14.2.2 Filters Combined with Boolean Logic;285
15.1.1.3;14.3 Case of Malaria In Silico Target Discovery;286
15.1.1.3.1;14.3.1 Targets Are Somewhere in Genomic and Postgenomic Databases;286
15.1.1.3.2;14.3.2 Translating Working Hypotheses into Boolean Searches;287
15.1.1.3.3;14.3.3 In Silico Target Discovery Tools;289
15.1.1.3.4;14.3.4 Toward Druggable Plasmodium Genome;290
15.1.1.4;14.4 Strategies to Identify and Select Drug Candidates;292
15.1.1.4.1;14.4.1 In Silico and In Vitro Drug Discovery;292
15.1.1.4.2;14.4.2 Structure-Based Drug Discovery;292
15.1.1.4.3;14.4.3 Target Similarity Searching, Substructure Searching,and QSAR;300
15.1.1.5;14.5 Grid Infrastructures for In Silico Drug Discovery;300
15.1.1.6;14.6 Conclusions;302
15.1.2;References;302
16;Sintchenko_Ch15_O.pdf;308
16.1;Chapter 15;308
16.1.1;Informatics for Healthcare Epidemiology;308
16.1.1.1;15.1 Introduction;308
16.1.1.2;15.2 Performance Measurement and Healthcare Associated Infections;308
16.1.1.3;15.3 Electronic Health Records;309
16.1.1.4;15.4 Building Databases for Healthcare Infection Control;311
16.1.1.4.1;15.4.1 Standards in Healthcare Informatics;311
16.1.1.4.2;15.4.2 Data Auditing and Validation;313
16.1.1.5;15.5 Information Systems for Healthcare Epidemiology;315
16.1.1.5.1;15.5.1 Use of Hit for Measurement;315
16.1.1.5.2;15.5.2 Monitoring Infection Control Interventions;317
16.1.1.5.3;15.5.3 Decision Support;317
16.1.1.6;15.6 Reporting Tools;319
16.1.1.7;15.7 Concluding Remarks;321
16.1.2;References;321
17;Sintchenko_Ch16_O.pdf;325
17.1;Chapter 16;325
17.1.1;Automated, High-throughput Surveillance Systems for Public Health;325
17.1.1.1;16.1 Introduction;325
17.1.1.2;16.2 Evaluation of Surveillance Systems;327
17.1.1.3;16.3 Surveillance Goals;327
17.1.1.4;16.4 Notifiable Disease Surveillance;328
17.1.1.4.1;16.4.1 Deficiencies in Existing Systems;328
17.1.1.4.2;16.4.2 Challenges in Automated Disease Detection;329
17.1.1.5;16.5 Syndromic Surveillance;330
17.1.1.5.1;16.5.1 Syndromes in Place of Specific Diseases;330
17.1.1.5.2;16.5.2 Choice of Syndromes and ICD Code Groupings;331
17.1.1.5.3;16.5.3 Early Detection and Alerting;332
17.1.1.5.4;16.5.4 Statistical Challenges;332
17.1.1.6;16.6 Adverse Event Surveillance;334
17.1.1.6.1;16.6.1 Vaccine Adverse Event Surveillance;334
17.1.1.6.2;16.6.2 Medication Adverse Event Surveillance;334
17.1.1.7;16.7 Non-Specific Biosurveillance;335
17.1.1.7.1;16.7.1 Non-Health Related Data Sources;336
17.1.1.7.2;16.7.2 The Challenge of Opportunity Cost;336
17.1.1.8;16.8 Finding and Harnessing Data;337
17.1.1.9;16.9 High Throughput Distributed Surveillance;338
17.1.1.10;16.10 Technical Aspects of Secure and Controlled Data Sharing;339
17.1.1.10.1;16.10.1 The Globus Toolkit;339
17.1.1.10.2;16.10.2 Internet Security;340
17.1.1.11;16.11 Examples of Public Health Surveillance Systems;341
17.1.1.11.1;16.11.1 The National Bioterrorism Syndromic Surveillance Program;341
17.1.1.11.2;16.11.2 The Electronic Medical Record Support for Public Health Project;341
17.1.1.11.3;16.11.3 The ESP Vaccine Adverse Event Reporting System;343
17.1.1.11.4;16.11.4 The Distributed Research Network;343
17.1.1.12;16.12 Concluding Remarks;344
17.1.2;References;346
18;Sintchenko_Ch17_O.pdf;347
18.1;Chapter 17;347
18.1.1;Microbial Genotyping Systems for Infection Control;347
18.1.1.1;17.1 Introduction;347
18.1.1.2;17.2 Hospital Infection Control Surveillance;348
18.1.1.3;17.3 Targeted Genotyping to Confirm Nosocomial Outbreaks;349
18.1.1.4;17.4 Universal Genotyping in Hospital Infection Control;351
18.1.1.5;17.5 Analysis of Genotyping Results;354
18.1.1.6;17.6 Choosing Typing Method for Genotyping Systems;355
18.1.1.6.1;Box 17.1 Typing methods;355
18.1.1.7;17.7 Integrating Genotyping with Surveillance Systems;356
18.1.1.8;17.8 Conclusion;358
18.1.2;References;358
19;Sintchenko_Ch18_O.pdf;361
19.1;Chapter 18;361
19.1.1;Temporal and Spatial Clustering of Bacterial Genotypes;361
19.1.1.1;18.1 Introduction;361
19.1.1.2;18.2 Detection of Spatio-Temporal Clusters;361
19.1.1.2.1;18.2.1 Temporal Surveillance Methods;362
19.1.1.2.2;18.2.2 Spatio-Temporal Surveillance Methods;364
19.1.1.3;18.3 New Surveillance Data Types;365
19.1.1.4;18.4 Infectious Disease Surveillance Using Genotype Clustering;366
19.1.1.4.1;18.4.1 Outbreak Definitions;366
19.1.1.4.2;18.4.2 Clustering Cases of Foodborne Disease;367
19.1.1.5;18.5 Concluding Remarks;370
19.1.2;References;372
20;Sintchenko_Ch19_O.pdf;374
20.1;Chapter 19;374
20.1.1;Infectious Disease Ontology;374
20.1.1.1;19.1 Vocabulary Resources for Biomedicine;374
20.1.1.2;19.2 Types of Vocabulary Resources;376
20.1.1.3;19.3 Features of Ontologies Needed to Support Informatics;378
20.1.1.4;19.4 Uses of Ontologies in Informatics-Driven Research and Care;381
20.1.1.5;19.5 Vocabulary Resources Relevant to the Field of Infectious Diseases;386
20.1.1.5.1;19.5.1 Medical Subject Headings Controlled Vocabulary;386
20.1.1.5.2;19.5.2 International Classification of Diseases;387
20.1.1.5.3;19.5.3 The Systematized Nomenclature of Medicine – Clinical Terms;388
20.1.1.5.4;19.5.4 The Disease Ontology;389
20.1.1.5.5;19.5.5 General Conclusions Concerning Clinical Vocabularies;389
20.1.1.5.6;19.5.6 The Gene Ontology and OBO Foundry Ontologies;390
20.1.1.5.7;19.5.7 Inadequacy of Current Resources;391
20.1.1.6;19.6 The Infectious Disease Ontology Consortium;391
20.1.1.7;19.7 Conclusions;393
20.1.2;References;393
21;Sintchenko_Ch20_O.pdf;397
21.1;Chapter 20;397
21.1.1;Populations, Patients, Germs and Genes: Ethics Of Genomics and Informatics in Communicable Disease Control;397
21.1.1.1;20.1 Introduction;397
21.1.1.2;20.2 Infectious Diseases Ethics;398
21.1.1.3;20.3 Challenges in Infectious Diseases Genomics Research;400
21.1.1.3.1;20.3.1 Genetics and Disease Susceptibility;400
21.1.1.3.2;20.3.2 The Malaria Genomic Epidemiology Network;401
21.1.1.3.3;20.3.3 The Human Microbiome Project;402
21.1.1.4;20.4 Application of Pathogenomics and Informatics Research to Communicable Disease Diagnostics and Prevention;404
21.1.1.4.1;20.4.1 Diagnostics and Antibiotic Resistance: Ethical Implications;404
21.1.1.4.2;20.4.2 Strain Typing for Pathogen Tracking;408
21.1.1.5;20.5 Information Science and Technology for Patient Management and Communicable Disease Control;409
21.1.1.5.1;20.5.1 Health Information Systems;409
21.1.1.5.2;20.5.2 Practical Application;410
21.1.1.6;20.6 Ethical Implication of Improvements in Biosurveillance;412
21.1.1.6.1;20.6.1 Electronic Patient Records;412
21.1.1.6.2;20.6.2 Communicable Disease Notification and Surveillance;413
21.1.1.6.3;20.6.3 The Use of New Laboratory Data;414
21.1.1.6.4;20.6.4 Surveillance Ethics: A New Paradigm;416
21.1.2;References;416
22;Sintchenko_BM_O.pdf;419
23;Sintchenko_Index_O.pdf;425