Statistics Applied to Bioinformatics

A text based on the author's Masters level course on statistics applied to bioinformatics aimed at graduate students from statistics, bioinformatics and biology. Filling a gap in the current literature, the text includes a rigorous and extensive background in biostatistics, along with detailed explanation of the key biological questions before focusing on the statistical analysis. Including exercises, numerous illustrations, extensive end of chapter summaries and examples of programming in R (a freely availabe statistical package), this is an ideal study text.

Contents
Introduction
Statistics and bioinformatics
Scope and approach of the book
Acknowledgements
Supporting material
Warning
Bioinformatics and Biology
Sequence searchers in databases
Gene prediction
Detection of sequence motifs
Comparative genomics
Expression profiles
Networks of interaction
Sequence Analysis
Combinatorial analysis
Descriptive statistics
Elements of probabilities
Theoretical distributions of probabilities
Sampling and estimation
Statistical tests
Hidden Markov Models (HMM)
An information theory perspective
Transcriptome
Introduction to multivariate analysis
Correlation analysis
Regression analysis
Analysis of variance
Unsupervised classification (Clustering
Reduction of dimensionality and visualization
Supervised classification (discriminant analysis)
Genome Evolution
Biochemical Networks
Exercises
Annexes
Formula tables
Index
References
Table of Contents provided by Publisher. All Rights Reserved.

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Statistics Applied to Bioinformatics

Summary

Table of Contents

Supplemental Materials