Introduction to Machine Learning and Bioinformatics / Edition 1

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 & Bioinformatics

The 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 Problems

Shedding 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.

Product Details

ISBN-13:	9780367387235
Publisher:	Taylor & Francis
Publication date:	09/19/2019
Series:	Chapman & Hall/ CRC Computer Science & Data Analysis
Pages:	384
Product dimensions:	6.12(w) x 9.19(h) x (d)

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