Note: Not guaranteed to come with supplemental materials (access cards, study guides, lab manuals, CDs, etc.)
Extend Your Rental at Any Time
Need to keep your rental past your due date? At any time before your due date you can extend or purchase your rental through your account.
Sorry, this item is currently unavailable.
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.
Table of Contents
Road Statistical Bioinformatics
High-Dimensional Biological Data
Small-n and Large-p problem
Noisy High-Throughput Biological Data
Integration of multiple, Heterogeneous Biological Data Information References
Probability Concepts and Distributions for analyzing Large Biological Data
Conditional Probability and Independence
Expected Value and Variance
Distributions of Random Variable
Joint and Marginal Distribution
Quality Control of High-Throughput Biological Data
Sources of Error in High-Throughput Biological Experiments
Statistical Techniques for Quality Control
Issues specific to Microarray Gene Expression Experiments
Statistical Testing and Significance for Large Biological Data Analysis
Real Data Analysis
Clustering: Unsupervised Learning in Large Biological Data
Measure of Similarity
Assessment of Cluster Quality
Classification: Supervised Learning with High-Dimensional Biological Data
Classification and Prediction Methods
Feature Selection and Ranking
Enhancement of Class Prediction by Ensemble Voting Methods
Comparison of Classification Methods Using High-Dimension Data
Software Examples for Classification Methods
Multidimensional Analysis and Visualization on Large Biomedical Data