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.