Introduction to Machine Learning and Bioinformatics

Sushmita Mitra, Sujay Datta, Theodore Perkins, George Michailidis

June 5, 2008 by Chapman and Hall/CRC
Textbook - 384 Pages - 62 B/W Illustrations
ISBN 9781584886822 - CAT# C682X
Series: Chapman & Hall/CRC Computer Science & Data Analysis


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  • Summarizes the latest developments in the fields of bioinformatics and machine learning
  • Provides background on the major problems in bioinformatics
  • Explains the concepts and algorithms of machine learning
  • Uses an abundance of realistic examples to demonstrate the capabilities of key machine learning techniques, such as hidden Markov models and artificial neural networks
  • Applies state-of-the-art machine learning techniques to bioinformatics problems in structural biology, cancer treatment, and proteomics
  • Offers PowerPoint slides and data sets on the authors’ website
  • Summary

    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.


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