Introduction to Machine Learning and Bioinformatics: 1st Edition (Paperback) book cover

Introduction to Machine Learning and Bioinformatics

1st Edition

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

Chapman and Hall/CRC

384 pages

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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.

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.

About the Authors

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

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Machine Theory
MATHEMATICS / Probability & Statistics / General