Biomedical Informatics in Translational Research

Name: Biomedical Informatics in Translational Research
Brand: Artech House
Price: 72.99 EUR
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Hai Hu(Editor)

Artech House (Publisher)

1st Edition

Published on 1. January 2008

270 pages

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978-1-59693-039-1 (ISBN)

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Biomedical Informatics in Translational Research
Contents
Preface
Chapter 1 Biomedical Informatics in TranslationalResearch
1.1 Evolution of Terminology
1.1.1 Translational Research
1.1.2 Systems Biology
1.1.3 Personalized Medicine
References
Chapter 2 The Clinical Perspective
2.1 Introduction
2.2 Ethics in Clinical Research
2.3 Regulatory Policies for Protecting a Research Subject's Privacy
2.4 Informed Consent
2.5 Collecting Clinical Data: Developing and Administering Survey Instruments
2.6 Issues Important to Biomedical Informatics
2.6.1 Data Tracking and Centralization
2.6.2 Deidentifying Data
2.6.3 Quality Assurance
2.6.4 Data Transfer from the Health Care Clinic to the Research Setting
2.7 Standard Operating Procedures
2.8 Developing and Implementing a Research Protocol
2.8.1 Developing a Research Protocol
2.8.2 Implementing the Research Protocol
2.9 Summary
References
Chapter 3 Tissue Banking: Collection, Processing, and Pathologic Characterization of Biospecimens for Research
3.1 Introduction
3.1.1 A Biorepository's Mandate
3.1.2 Overview of Current Tissue Banking Practices
3.2 Consenting and Clinical Data Acquisition
3.3 Blood Collection, Processing, and Storage
3.4 Tissue Collection, Processing, Archiving, and Annotation
3.4.1 Tissue Collection
3.4.2 Tissue Processing
3.4.3 Tissue Archiving and Storage
3.4.4 Pathologic Characterization of Tissue Samples
3.5 Conclusion
References
Chapter 4 Biological Perspective
4.1 Background for "Omics" Technologies
4.2 Basic Biology and Definitions
4.2.1 A Historical Perspective
4.2.2 Biological Processes
4.2.3 Some Definitions
4.3 Very Basic Biochemistry
4.3.1 DNA
4.3.2 RNA
4.3.3 Proteins
4.4 Summary
References
Chapter 5 Genomics Studies
5.1 Introduction
5.2 Genomic Technologies Used for DNA Analysis
5.2.1 DNA Sequencing
5.2.1.2 Biomedical Informatics Requirements
5.2.1.3 Future Directions
5.2.2 Genotyping
5.2.2.1 Array Technologies
5.2.2.2 Technological Assessment of Genotyping
5.2.2.3 Affymetrix Genotyping SNP Assay Workflow
5.2.2.4 QA/SOP Issues
5.2.2.5 Biomedical Informatics Requirements
5.2.2.6 Future Directions
5.2.3 Array-Based Comparative Genomic Hybridization
5.2.3.1 Technological Assessment of Chromosomal Rearrangements
5.2.3.2 Example Platform
5.2.3.3 QA/SOP Issues
5.2.3.4 Biomedical Informatics Requirements
5.2.3.5 Oligo-Based aCGH Platform
5.3 Genomic Technology Used for RNA Analysis
5.3.1 Real-Time PCR
5.3.1.1 Data Analysis Methods
5.3.1.2 Biomedical Informatics Requirements
5.3.1.3 Future Directions
5.3.2 Microarrays
5.3.2.1 Array Technologies
5.3.2.2 Example Platform
5.3.2.3 QA/SOP Issues
5.3.2.4 MIAME Checklist and Platform Comparison
5.3.2.5 Data Analysis Issues
5.3.2.6 Biomedical Informatics Requirements
5.3.2.7 Future Directions
5.3.3 Chips for Alternative Splicing Analysis (GeneChip Exon)
5.3.3.1 Array Technology
5.3.3.2 Biomedical Informatics Requirements
5.3.3.3 Future Directions
5.4 Translational Research Case Studies
5.4.1 Case 1
5.4.2 Case 2
5.5 Summary
References
Chapter 6 Proteomics
6.1 Introduction
6.2 Clinical Specimens
6.2.1 Body Fluids
6.2.2 Tissue
6.2.1.1 Blood
6.2.1.2 Urine
6.2.1.3 Cerebrospinal Fluid
6.3 Proteomics Technologies
6.3.1 Two-Dimensional Gel Electrophoresis
6.3.2 MALDI-TOF
6.3.3 Liquid Chromatography Mass Spectrometry
6.3.3.1 Shotgun Proteomics
6.3.3.2 Characterization of Posttranslational Modification of Proteins Using the Shotgun Approach
6.3.4 Protein Arrays
6.4 Analysis of Proteomics Data
6.4.1 2D DIGE Data Analysis
6.4.2 SELDI-TOF/MALDI-TOF Data Analysis
6.4.3 Shotgun Proteomics Data Analysis
6.5 Summary
References
Chapter 7 Data Tracking Systems
7.1 Introduction
7.1.1 Definition of a Data Tracking System
7.1.2 Why Use a Data Tracking System?
7.2 Overview of Data Tracking Systems
7.2.1 Historical Review
7.2.2 Available Resources
7.2.3 Data Tracking Systems in the Life Sciences
7.2.3.1 Clinical Data Collection
7.2.3.2 Tissue Processing and Banking
7.2.3.3 Data Tracking for Genomics and Proteomics Studies
7.3 Major Requirements of a Data Tracking System for Biomedical Informatics Research
7.3.1 General Requirements
7.3.2 Front-End Requirements
7.3.3 Back-End Requirements
7.3.4 Field-Specific Requirements
7.3.4.1 Requirements for Clinical Data Tracking
7.3.4.2 Requirements for Tissue Banking and Specimen Preparation
7.3.4.3 Requirements for Genomics Experiments
7.3.4.4 Requirements for Proteomics Experiments
7.3.5 Additional Points
7.4 Ways to Establish a Data Tracking System
7.4.1 Buy a System Off the Shelf
7.4.2 Develop a System
7.4.3 Pursue a Hybrid Approach
7.5 Deployment Challenges and Other Notes
7.5.1 Resistance from End Users
7.5.2 Training
7.5.3 Mismatches Between System Features and Real Needs
7.5.4 Protocol Changes and Other Evolutions
7.5.5 Data Tracking System as a Data Source
7.6 Summary
References
Chapter 8 Data Centralization
8.1 An Overview of Data Centralization
8.2 Types of Data in Question
8.2.1 in-house Patient-Centric Clinical, Genomic, and Proteomic Data
8.2.2 Publicly Available Annotation and Experimental Data
8.2.2.1 Popular Public Databases
8.2.2.2 Public Databases of Special Values
8.2.2.3 Considerations and Concerns About Using or Integrating Public Data
8.2.3 Data Format Standards
8.3 DW Development for Integrative Biomedical Informatics Research
8.3.1 Selection of the Developing Partner-Experiences in the Field
8.3.2 DW Requirements
8.3.3 Data Source Selection
8.3.4 Hardware and the Database Management Systems Selection
8.3.5 DW Structural Models-Integrated, Federated, or Hybrid
8.3.6 Data Models: Dimensional Models, Data Marts, and Normalization Levels
8.3.7 Data Models: EAV, Entity-Relationship, and Object-Oriented Modules
8.3.8 Data Models: Handling of the Temporal Information
8.3.9 Data Extraction, Cleansing, Transformation, and Loading
8.3.10 Tuning and QA
8.3.11 Changes-The Dynamic Nature
8.4 Use of the DW
8.5 Example Case
8.6 Summary
References
Chapter 9 Data Analysis
9.1 The Nature and Diversity of Research Data in Translational Medicine
9.1.1 Where Data Reside
9.1.2 Operational Versus Analytical Data Systems
9.1.3 Data Warehouses
9.1.4 Data Preprocessing
9.1.4.1 Missing Value Handling
9.1.4.2 Outlier Removal (Clipping)
9.1.4.3 Binning
9.1.4.4 Normalization
9.2 Data Analysis Methods and Techniques
9.2.3 Predictive Modeling
9.2.3.1 Feature Selection
9.2.3.2 Regression
9.2.3.3 Classification
9.2.4 Clustering
9.2.4.1 Self-Organizing Maps
9.2.4.2 K-Means
9.2.4.3 Hierarchical
9.2.5 Evaluation and Validation Methodologies
9.2.5.1 Type I and Type II Errors
9.2.5.2 Sensitivity Versus Specificity
9.2.5.3 ROC Graph
9.2.5.4 Cross Validation
9.2.1 Generalized Forms of Analysis
9.2.1.1 Significance Testing
9.2.1.2 Predictive Modeling
9.2.1.3 Clustering
9.2.2 Significance Testing
9.2.2.1 Pearson's Chi-Square Test
9.2.2.2 Student's t-Test
9.2.2.3 ANOVA
9.2.2.4 Wilcoxon Signed-Rank Test
9.3 Analysis of High-Throughput Genomic and Proteomic Data
9.3.1 Genomic Data
9.3.1.1 Different Platforms Available
9.3.1.2 Quality Control
9.3.1.3 Image Analysis
9.3.1.4 Data Normalization and Transformation
9.3.1.5 Statistical Analysis
9.3.1.6 High-Level Analysis: Profiling or Pattern Recognition
9.3.2 Proteomic Data
9.3.3 Functional Determination
9.3.3.1 Gene Ontology
9.3.3.2 Pathway Analysis
9.4 Analysis of Clinical Data
9.5 Analysis of Textual Data
9.5.1 Data Sources
9.5.2 Biological Entity
9.5.3 Mining Relations Between Named Entities
9.6 Integrative Analysis and Application Examples
9.7 Data Analysis Tools and Resources
9.8 Summary
References
Chapter 10 Research and Application: Examples
10.1 Introduction
10.2 deCODE Genetics
10.2.1 Data Repository Development and Data Centralization
10.2.1.1 Clinical Data Collection: Patient Consent and Deidentification
10.2.2 Genomic Studies
10.2.2.1 Linkage Analysis
10.2.2.2 Association Studies
10.2.2.3 Limitations
10.2.2.4 Analysis Tool
10.2.3 Application
10.2.3.1 Drug Development Based on Identification of Genotype for Myocardial Infarction Risk
10.2.3.2 Development of a Diagnostic to Detect a Diabetes Risk Genotype
10.3 Windber Research Institute
10.3.1 Clinical Data Collection and Storage
10.3.1.1 The Clinical Breast Care Project
10.3.1.2 Integrative Cardiac and Metabolic Health Program
10.3.2 Data Tracking
10.3.3 Data Centralization
10.3.4 Genomic and Proteomic Studies
10.3.4.1 Allelic Imbalance to Characterize Genomic Instability
10.3.4.2 Proteomic Analysis of Cardiovascular Risk Factor Modification
10.3.5 Data Analysis, Data Mining, and Data Visualization
10.3.5.1 Knowledge Development Environment
10.3.5.2 Pathology Co-Occurrence Analysis
10.3.6 Outcomes Summary
10.4 Conclusions
References
Chapter 11 Clinical Examples: A Biomedical Informatics Approach
11.1 Understanding the Role of Biomarkers and Diagnostics
11.2 Understanding the Difference Between Pathways and Networks
11.3 How Biomarkers/Diagnostics and Pathways/Networks Are Linked
11.4 Breast Cancer
11.5 Menopause
11.6 Coagulation/DIC
11.7 Conclusions
References
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Biomedical Informatics in Translational Research

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