Biomedical Informatics in Translational Research

Front Cover
Artech House, 2008 - Computers - 264 pages
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This groundbreaking resource on biomedical informatics gives you step-by-step insight into innovative techniques for integrating and federating data from clinical and high-throughput molecular study platforms as well as from the public domain. It 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. Filling the need for informatics applications that bridge the clinical-basic domains and facilitate the bi-directional flow of research, this definitive volume offers a systems-oriented approach to the subject that complements the traditional bottom-up approach of systems biology. You get clear insight into how to conduct biomedical informatics research at both the clinical and molecular levels, with detailed guidelines on study design, IRB protocol development, questionnaire design, specimen collection, and other procedures and applications. The book explains the latest data integration and federation approaches, and points the way to potential new data analysis and mining methodologies for tackling problems that cannot be readily resolved using current technologies. Complete with in-depth case examples demonstrating how to develop tools for specific biomedical informatics tasks, this pioneering work will prove invaluable to your efforts in managing clinical and high-throughput data and making the most of targeted basic research.
 

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Contents

Biomedical Informatics in Translational Research
1
11 Evolution of Terminology
3
112 Systems Biology
4
References
9
The Clinical Perspective
11
22 Ethics in Clinical Research
12
23 Regulatory Policies for Protecting a Research Subjects Privacy
13
24 Informed Consent
15
75 Deployment Challenges and Other Notes
133
752 Training
134
753 Mismatches Between System Features and Real Needs
135
755 Data Tracking System as a Data Source
136
Data Centralization
141
81 An Overview of Data Centralization
142
82 Types of Data in Question
145
821 Inhouse PatientCentric Clinical Genomic and Proteomic Data
147

Developing and Administering Survey Instruments
17
26 Issues Important to Biomedical Informatics
18
262 Deidentifying Data
19
263 Quality Assurance
20
264 Data Transfer from the Health Care Clinic to the Research Setting
21
27 Standard Operating Procedures
23
281 Developing a Research Protocol
24
282 Implementing the Research Protocol
28
29 Summary
29
Tissue Banking Collection Processing and Pathologic Characterization of Biospecimens for Research
31
312 Overview of Current Tissue Banking Practices
32
32 Consenting and Clinical Data Acquisition
33
34 Tissue Collection Processing Archiving and Annotation
35
342 Tissue Processing
36
343 Tissue Archiving and Storage
37
344 Pathologic Characterization of Tissue Samples
39
35 Conclusion
41
Biological Perspective
43
42 Basic Biology and Definitions
44
423 Some Definitions
45
43 Very Basic Biochemistry
46
432 RNA
47
433 Proteins
50
44 Summary
52
Genomics Studies
55
52 Genomic Technologies Used for DNA Analysis
56
522 Genotyping
58
523 ArrayBased Comparative Genomic Hybridization
64
53 Genomic Technology Used for RNA Analysis
69
532 Microarrays
70
533 Chips for Alternative Splicing Analysis GeneChip Exon
76
54 Translational Research Case Studies
78
541 Case 1
79
55 Summary
80
Proteomics
85
62 Clinical Specimens
87
622 Tissue
89
63 Proteomics Technologies
90
631 TwoDimensional Gel Electrophoresis
91
632 MALDITOF
93
633 Liquid Chromatography Mass Spectrometry
95
634 Protein Arrays
101
64 Analysis of Proteomics Data
103
643 Shotgun Proteomics Data Analysis
104
65 Summary
105
Data Tracking Systems
111
712 Why Use a Data Tracking System?
112
72 Overview of Data Tracking Systems
113
722 Available Resources
114
73 Major Requirements of a Data Tracking System for Biomedical Informatics Research
119
731 General Requirements
120
733 BackEnd Requirements
121
735 Additional Points
126
74 Ways to Establish a Data Tracking System
127
742 Develop a System
129
743 Pursue a Hybrid Approach
132
822 Publicly Available Annotation and Experimental Data
149
823 Data Format Standards
155
83 DW Development for Integrative Biomedical Informatics Research
157
832 DW Requirements
158
833 Data Source Selection
159
834 Hardware and the Database Management Systems Selection
160
835 DW Structural ModelsIntegrated Federated or Hybrid
161
Dimensional Models Data Marts and Normalization Levels
162
Handling of the Temporal Information
164
839 Data Extraction Cleansing Transformation and Loading
166
8310 Tuning and QA
167
84 Use of the DW
168
85 Example Case
169
86 Summary
171
Data Analysis
175
91 The Nature and Diversity of Research Data in Translational Medicine
176
912 Operational Versus Analytical Data Systems
177
914 Data Preprocessing
178
92 Data Analysis Methods and Techniques
179
922 Significance Testing
180
923 Predictive Modeling
182
924 Clustering
184
925 Evaluation and Validation Methodologies
185
93 Analysis of HighThroughput Genomic and Proteomic Data
187
931 Genomic Data
188
932 Proteomic Data
190
933 Functional Determination
192
94 Analysis of Clinical Data
194
95 Analysis of Textual Data
195
952 Biological Entity
196
953 Mining Relations Between Named Entities
197
96 Integrative Analysis and Application Examples
199
97 Data Analysis Tools and Resources
200
98 Summary
202
Research and Application Examples
207
102 deCODE Genetics
208
1022 Genomic Studies
210
1023 Application
212
103 Windber Research Institute
214
1031 Clinical Data Collection and Storage
215
1032 Data Tracking
216
1033 Data Centralization
217
1035 Data Analysis Data Mining and Data Visualization
219
1036 Outcomes Summary
222
104 Conclusions
224
Clinical Examples A Biomedical Informatics Approach
227
112 Understanding the Difference Between Pathways and Networks
228
114 Breast Cancer
229
115 Menopause
233
116 CoagulationDIC
240
117 Conclusions
247
About the Editors
249
About the Contributors
250
Index
255
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About the author (2008)

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.

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