Statistical Bioinformatics For Biomedical and

This practical introduction clearly presents the underlying statistical concepts and techniques critical for successful use of bioinformatics tools in biomedical research without requiring an advanced background in math/statistics.

Jae K. Lee, Ph.D., is a professor of biostatistics and epidemiology in the Department of Health Evaluation Sciences at the University of Virginia School of Medicine, where he designed and teaches a course on Statistical Bioinformatics in Medicine. He earned his doctorate in statistical genetics from the University of Wisconsin, Madison. He was previously a research scientist in the Laboratory of Molecular Pharmacology, National Cancer Institute. Among his current research interests is the integration of statistical and genomic information for the analysis of microarray data.

Preface	p. xi
Contributors	p. xiii
Road Statistical Bioinformatics	p. 1
Multiple-Comparisons Issue	p. 1
High-Dimensional Biological Data	p. 2
Small-n and Large-p problem	p. 3
Noisy High-Throughput Biological Data	p. 3
Integration of multiple, Heterogeneous Biological Data Information References	p. 5
Probability Concepts and Distributions for analyzing Large Biological Data	p. 7
Introduction	p. 7
Basic Concepts	p. 8
Conditional Probability and Independence	p. 10
Random Variables	p. 13
Expected Value and Variance	p. 15
Distributions of Random Variable	p. 19
Joint and Marginal Distribution	p. 39
Multivariate Distribution	p. 42
Sampling Distribution	p. 46
Summary	p. 54
Quality Control of High-Throughput Biological Data	p. 57
Sources of Error in High-Throughput Biological Experiments	p. 57
Statistical Techniques for Quality Control	p. 59
Issues specific to Microarray Gene Expression Experiments	p. 66
Conclusion	p. 69
References	p. 69
Statistical Testing and Significance for Large Biological Data Analysis	p. 71
Introduction	p. 71
Statistical Testing	p. 72
Error Controlling	p. 78
Real Data Analysis	p. 81
Concluding Remarks	p. 87
Acknowledgement	p. 87
References	p. 87
Clustering: Unsupervised Learning in Large Biological Data	p. 89
Measure of Similarity	p. 90
Clustering	p. 99
Assessment of Cluster Quality	p. 115
Conclusion	p. 123
References	p. 123
Classification: Supervised Learning with High-Dimensional Biological Data	p. 129
Introduction	p. 129
Classification and Prediction Methods	p. 132
Feature Selection and Ranking	p. 140
Cross-Validation	p. 144
Enhancement of Class Prediction by Ensemble Voting Methods	p. 145
Comparison of Classification Methods Using High-Dimension Data	p. 147
Software Examples for Classification Methods	p. 150
References	p. 154
Multidimensional Analysis and Visualization on Large Biomedical Data	p. 157
Introduction	p. 157
Classical Multidimensional Visualization Techniques	p. 158
Two-Dimensional Projections	p. 161
Issues and Challenges	p. 165
Systematic Exploration of Low Dimensional Projections	p. 166
One-Dimensional Histogram Ordering	p. 170
Two-Dimensional Histogram Ordering	p. 174
Conclusion	p. 181
References	p. 182
Statistical Models, Inferences, and Algorithms for Large Biological Data Analysis	p. 185
Introduction	p. 185
Statistical/Problematic Models	p. 187
Estimation Methods	p. 189
Numerical Algorithms	p. 191
Examples	p. 192
Conclusion	p. 198
References	p. 199
Expoerimental Designs on High-Throughput Biological Experiments	p. 201
Randomization	p. 201
Replication	p. 202
Pooling	p. 209
Blocking	p. 210
Design for Classifications	p. 214
Design for Time Course Experiments	p. 215
Design for eQTL Studies	p. 215
Reference	p. 216
Statistical Resampling Techniques for Large Biological Data Analysis	p. 219
Introduction	p. 219
Resampling Methods for Prediction Error Assessment and Model Selection	p. 221
Feature Selection	p. 225
Resampling-Based Classification Algorithms	p. 226
Practical Example: Lymphoma	p. 226
Resampling Methods	p. 227
Bootstrap Methods	p. 232
Sample Size Issues	p. 233
Loss Functions	p. 235
Bootstrap Resampling for Quantifying Uncertainty	p. 236
Markov Chain Monte Carlo Methods	p. 238
Conclusion	p. 240
References	p. 247
Statistical Network Analysis for Biological Systems and Pathways	p. 249
Introduction	p. 249
Boolean Network Modeling	p. 250
Bayesian Belief Network	p. 259
Modeling of Metabolic Networks	p. 273
References	p. 279
Trends and Statistical Challenges in Genomewide Association Studies	p. 283
Introduction	p. 283
Alles, Linkage Disequilibrium, and Haplotype	p. 283
International Hap Map Project	p. 285
Genotyping Platforms	p. 286
Overview of Current GWAS Results	p. 287
Statistical Issues in GWAS	p. 290
Haplotype Analysis	p. 296
Homozygosity and Admixture Mapping	p. 298
Gene x Gene and Gene x Environmental Interactions	p. 298
Gene and Pathway-Based Analysis	p. 299
Disease Risk Estimates	p. 301
Meta-Analysis	p. 301
Rare Variants and Sequence-Based Analysis	p. 302
Conclusions	p. 303
Acknowledgment	p. 303
References	p. 303
Rand Bioconductor Packages in Bioinformatics: Towards System Biology	p. 309
Introduction	p. 309
Brief Overview of the Bioconductor Project	p. 310
Experimental Data	p. 311
Annotation	p. 318
Models of Biological Sytems	p. 328
Conclusion	p. 335
Acknowledgment	p. 336
Refernces	p. 336
Index	p. 339
Table of Contents provided by Ingram. All Rights Reserved.

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

FREE SHIPPING

Amazon no longer offers textbook rentals. We do!

Statistical Bioinformatics For Biomedical and Life Science Researchers

Summary

Author Biography

Table of Contents

Supplemental Materials