Introduction to Machine Learning and Bioinformatics

by Mitra; Sushmita

• ISBN13: 9781584886822
• ISBN10: 158488682X
• Edition: 1st
• Format: Hardcover
• Copyright: 2008-06-05
• Publisher: Chapman & Hall/

Summary

Lucidly Integrates Current ActivitiesFocusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformaticspresents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.Examines Connections between Machine Learning & BioinformaticsThe book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website.Explores How Machine Learning Techniques Can Help Solve Bioinformatics ProblemsShedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today's biological experiments.

Table of Contents

Introduction	p. 1
The biology of a living organism	p. 5
Probabilistic and model-based learning	p. 25
Classification techniques	p. 101
Unsupervised learning techniques	p. 129
Computational intelligence in bioinformatics	p. 155
Connections between machine learning and bioinformatics	p. 211
Machine learning in structural biology : interpreting 3D protein images	p. 237
Soft computing in biclustering	p. 277
Bayesian machine-learning methods for tumor classification using gene expression data	p. 303
Modeling and analysis of quantitative proteomics data obtained from iTRAQ experiments	p. 327
Statistical methods for classifying mass spectrometry database search results	p. 339
References	p. 357
Index	p. 361
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