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"Kinetics of High-Temperature Processes "

Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology.Following three introductory chapters -- an introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biology -- the book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods.

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Series Title
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Publisher
Computational biology.;Kernel : Cambridge, Mass. : MIT Press,.,
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Language
English
ISBN/ISSN
9780262310819
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NONE
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text
Media Type
computer
Carrier Type
online resource
Edition
1
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Cataloger
Suwardi
Source
https://direct.mit.edu/books/edited-volume/3898/Kernel-Methods-in-Computational-Biology
Validator
Suwardi
Digital Object Identifier (DOI)
https://doi.org/10.7551/mitpress/4057.001.0001
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