MACHINE LEARNING IN BIOINFORMATICS by Yanqing Zhang, Jagath C. Rajapakse ( 9780470116623 )

Item Details

Title:

MACHINE LEARNING IN BIOINFORMATICS

By:

Yanqing Zhang, Jagath C. Rajapakse

Format:

Hardback

List price:

£122.95

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£110.66

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10% off

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£12.29

ISBN 10:

0470116625

ISBN 13:

9780470116623

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Publisher:

JOHN WILEY AND SONS LTD

Pub. date:

3 December, 2008

Series:

Wiley Series in Bioinformatics

Pages:

456

Description:

Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc. , have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization.

Synopsis:

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.

Illustrations:

Illustrations

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Imprint:

Wiley-Blackwell

Returns:

Returnable

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