Machine Learning in Bioinformatics

Machine Learning in Bioinformatics

Editor(s): Yan-Qing Zhang, Jagath C. Rajapakse

Published Online: 21 APR 2008

Print ISBN: 9780470116623

Online ISBN: 9780470397428

DOI: 10.1002/9780470397428

Series Editor(s): Yi Pan, Albert Y. Zomaya

About this Book

An introduction to machine learning methods and their applications to problems in bioinformatics

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.

From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more.

Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

Table of contents

    1. You have free access to this content
    2. Chapter 7

      Sequence-Based Prediction of Residue-Level Properties in Proteins (pages 157–187)

      Shandar Ahmad, Yemlembam Hemjit Singh, Marcos J. Araúzo-Bravo and Akinori Sarai

    3. Chapter 20

      An Information Fusion Framework for Biomedical Informatics (pages 431–451)

      Srivatsava R. Ganta, Anand Narasimhamurthy, Jyotsna Kasturi and Raj Acharya

    4. You have free access to this content