Introduction to Machine Learning and Bioinformatics

by Sushmita Mitra, Sujay Datta, Theodore Perkins, George Michailidis

Overview

Lucidly Integrates Current Activities

Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.

Examines Connections between Machine Learning & Bio

Product Details

ISBN-13:	9781040170618
Publisher:	CRC Press
Publication date:	06/05/2008
Sold by:	Barnes & Noble
Format:	eBook
Pages:	384
File size:	5 MB

About the Author

Mitra, Sushmita; Datta, Sujay; Perkins, Theodore; Michailidis, George

Table of Contents

Introduction. The Biology of a Living Organism. Probabilistic and Model-Based Learning. Classification Techniques. Unsupervised Learning Techniques. Computational Intelligence in Bioinformatics. Connections. Machine Learning in Structural Biology. Soft Computing in Biclustering. Bayesian Methods for Tumor Classification. Modeling and Analysis of iTRAQ Data. Mass Spectrometry Classification. Index.

What People are Saying About This

From the Publisher

… The stated audience for this book is M.S. and Ph.D. students in bioinformatics, machine intelligence, applied statistics, biostatistics, computer science, and related areas. … a well-written collection from multiple authors that I recommend for the intended audience. Several chapters include exercises.
—Technometrics, November 2009, Vol. 51, No. 4

…a good text/reference book that summarizes the latest developments in the interface between bioinformatics and machine learning and offer[s] a thorough introduction to each field. … One of the strengths of this book is the clear notation with a mathematical and statistical flavor, which will be attractive to Biometrics readers, especially to those new to statistical learning and data mining. It is also very readable for a variety of interested learners, researchers, and audiences from various backgrounds and disciplines. …
—Biometrics, March 2009

… a well-structured book that is a good starting point for machine learning in bioinformatics. … Using many popular examples, the statistical theory becomes comprehensible and bioinformatics examples motivate [readers] to apply the concepts to real data.
—Markus Schmidberger, Journal of Statistical Software, November 2008

Editorial Reviews

… The stated audience for this book is M.S. and Ph.D. students in bioinformatics, machine intelligence, applied statistics, biostatistics, computer science, and related areas. … a well-written collection from multiple authors that I recommend for the intended audience. Several chapters include exercises.
—Technometrics, November 2009, Vol. 51, No. 4

…a good text/reference book that summarizes the latest developments in the interface between bioinformatics and machine learning and offer[s] a thorough introduction to each field. … One of the strengths of this book is the clear notation with a mathematical and statistical flavor, which will be attractive to Biometrics readers, especially to those new to statistical learning and data mining. It is also very readable for a variety of interested learners, researchers, and audiences from various backgrounds and disciplines. …
—Biometrics, March 2009

… a well-structured book that is a good starting point for machine learning in bioinformatics. … Using many popular examples, the statistical theory becomes comprehensible and bioinformatics examples motivate [readers] to apply the concepts to real data.
—Markus Schmidberger, Journal of Statistical Software, November 2008

From the Publisher