Biomedical Informatics in Translational Research

by Hai Hu, Michael Liebman

Overview

This groundbreaking biomedical informatics resource offers step-by-step guidance on innovative techniques for integrating and federating data from clinical and high-throughput molecular study platforms, as well as from the public domain. This unique book details how to apply computational and statistical technologies to clinical, genomic, and proteomic studies to enhance data collection, tracking, storage, visualization, analysis, and knowledge discovery processes, and to translate knowledge from "bench to bedside" and "bedside to bench" with never-before efficiency.

Taking a systems-oriented approach, the book offers insight into how to conduct biomedical informatics research at the clinical and molecular levels, with detailed guidelines on study design, IRB protocol development, questionnaire design, specimen collection, and other procedures and applications. Readers learn the latest data integration and federation approaches, and explore potential new data analysis and mining methodologies for tackling problems that cannot be readily resolved using current technologies. Moreover, this pioneering work includes in-depth examples, demonstrating how to develop tools for specific biomedical informatics tasks.

Product Details

ISBN-13:	9781596930384
Publisher:	Artech House, Incorporated
Publication date:	07/31/2008
Edition description:	New Edition
Pages:	364
Product dimensions:	7.20(w) x 10.20(h) x 0.80(d)

About the Author

Hai Hu is senior director of Biomedical Informatics and senior staff scientist at Windber Research Institute, Pennsylvania. He is former group leader/senior bioinformatics scientist at AxCell Biosciences and member of the adjunct faculty of Widener University, Chester, Pennsylvania. He earned his Ph.D. in biophysics at the State University of New York at Buffalo.

Michael Liebman is chief scientific officer at Windber Research Institute, Pennsylvania. He previously served as director, biomedical informatics at the Abramson Cancer Center, University of Pennsylvania, and as adjunct professor at the University of Pennsylvania, Northwestern University, and Loyola University Medical School. He received his Ph.D. in physical chemistry from Michigan State University.

Table of Contents

Preface     xiii
Biomedical Informatics in Translational Research     1
Evolution of Terminology     3
Translational Research     3
Systems Biology     4
Personalized Medicine     4
References     9
The Clinical Perspective     11
Introduction     11
Ethics in Clinical Research     12
Regulatory Policies for Protecting a Research Subject's Privacy     13
Informed Consent     15
Collecting Clinical Data: Developing and Administering Survey Instruments     17
Issues Important to Biomedical Informatics     18
Data Tracking and Centralization     18
Deidentifying Data     19
Quality Assurance     20
Data Transfer from the Health Care Clinic to the Research Setting     21
Standard Operating Procedures     23
Developing and Implementing a Research Protocol     23
Developing a Research Protocol     24
Implementing the Research Protocol     28
Summary     29
References     29
Tissue Banking: Collection, Processing, and Pathologic Characterization of Biospecimens for Research     31
Introduction     31
A Biorepository's Mandate     31
Overview of Current Tissue Banking Practices     32
Consenting and Clinical Data Acquisition     33
Blood Collection, Processing, and Storage     33
Tissue Collection, Processing, Archiving, and Annotation     35
Tissue Collection     35
Tissue Processing     36
Tissue Archiving and Storage     37
Pathologic Characterization of Tissue Samples     39
Conclusion     41
References     41
Biological Perspective     43
Background for "Omics" Technologies     43
Basic Biology and Definitions     44
A Historical Perspective     44
Biological Processes     44
Some Definitions     45
Very Basic Biochemistry     46
DNA     46
RNA     47
Proteins     50
Summary     52
References     52
Genomics Studies     55
Introduction     55
Genomic Technologies Used for DNA Analysis     56
DNA Sequencing     56
Genotyping     58
Array-Based Comparative Genomic Hybridization      64
Genomic Technology Used for RNA Analysis     69
Real-Time PCR     69
Microarrays     70
Chips for Alternative Splicing Analysis (GeneChip Exon)     76
Translational Research Case Studies     78
Case 1     79
Case 2     79
Summary     80
References     80
Proteomics     85
Introduction     85
Clinical Specimens     87
Body Fluids     87
Tissue     89
Proteomics Technologies     90
Two-Dimensional Gel Electrophoresis     91
MALDI-TOF     93
Liquid Chromatography Mass Spectrometry     95
Protein Arrays     101
Analysis of Proteomics Data     103
2D DIGE Data Analysis     103
SELDI-TOF/MALDI-TOF Data Analysis     103
Shotgun Proteomics Data Analysis     104
Summary     105
References     105
Data Tracking Systems     111
Introduction     111
Definition of a Data Tracking System     111
Why Use a Data Tracking System?     112
Overview of Data Tracking Systems      113
Historical Review     113
Available Resources     114
Data Tracking Systems in the Life Sciences     114
Major Requirements of a Data Tracking System for Biomedical Informatics Research     119
General Requirements     120
Front-End Requirements     120
Back-End Requirements     121
Field-Specific Requirements     121
Additional Points     126
Ways to Establish a Data Tracking System     127
Buy a System Off the Shelf     127
Develop a System     129
Pursue a Hybrid Approach     132
Deployment Challenges and Other Notes     133
Resistance from End Users     133
Training     134
Mismatches Between System Features and Real Needs     135
Protocol Changes and Other Evolutions     135
Data Tracking System as a Data Source     136
Summary     136
References     136
Data Centralization     141
An Overview of Data Centralization     142
Types of Data in Question     145
In-house Patient-Centric Clinical, Genomic, and Proteomic Data     147
Publicly Available Annotation and Experimental Data      149
Data Format Standards     155
DW Development for Integrative Biomedical Informatics Research     157
Selection of the Developing Partner-Experiences in the Field     157
DW Requirements     158
Data Source Selection     159
Hardware and the Database Management Systems Selection     160
DW Structural Models-Integrated, Federated, or Hybrid     161
Data Models: Dimensional Models, Data Marts, and Normalization Levels     162
Data Models: EAV, Entity-Relationship, and Object-Oriented Modules     162
Data Models: Handling of the Temporal Information     164
Data Extraction, Cleansing, Transformation, and Loading     166
Tuning and QA     167
Changes-The Dynamic Nature     167
Use of the DW     168
Example Case     169
Summary     171
References     171
Data Analysis     175
The Nature and Diversity of Research Data in Translational Medicine     176
Where Data Reside     176
Operational Versus Analytical Data Systems     177
Data Warehouses     177
Data Preprocessing     178
Data Analysis Methods and Techniques     179
Generalized Forms of Analysis     179
Significance Testing     180
Predictive Modeling     182
Clustering     184
Evaluation and Validation Methodologies     185
Analysis of High-Throughput Genomic and Proteomic Data     187
Genomic Data     188
Proteomic Data     190
Functional Determination     192
Analysis of Clinical Data     194
Analysis of Textual Data     195
Data Sources     195
Biological Entity     196
Mining Relations Between Named Entities     197
Integrative Analysis and Application Examples     199
Data Analysis Tools and Resources     200
Summary     202
References     202
Research and Application: Examples     207
Introduction     207
deCODE Genetics     208
Data Repository Development and Data Centralization     208
Genomic Studies     210
Application     212
Windber Research Institute     214
Clinical Data Collection and Storage     215
Data Tracking     216
Data Centralization     217
Genomic and Proteomic Studies      217
Data Analysis, Data Mining, and Data Visualization     219
Outcomes Summary     222
Conclusions     224
References     224
Clinical Examples: A Biomedical Informatics Approach     227
Understanding the Role of Biomarkers and Diagnostics     227
Understanding the Difference Between Pathways and Networks     228
How Biomarkers/Diagnostics and Pathways/Networks Are Linked     228
Breast Cancer     229
Menopause     233
Coagulation/DIC     240
Conclusions     247
References     247
About the Editors     249
About the Contributors     250
Index     255