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Biological Data Mining

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Synopses & Reviews

Book News Annotation:

This reference provides comprehensive data mining concepts, theories, and applications in current medical and biological research. Features include extensive coverage of biological sequences, structures, Imics, ontology, literature mining, biological concepts integrated with data mining techniques, case studies of biological applications, and more. It is intended for graduate students, researchers, and practitioners. Editors are Chen (informatics, Indiana U. School of Informatics) and Lonardi (computer science and engineering, U. of California, Riverside. The book's 80 contributors are professionals in biological data mining research. Annotation ©2009 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplinary data mining researchers who cover state-of-the-art biological topics.

The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput Omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications.

This volume examines the concepts, problems, progress, and trends in developing and applying new data mining techniques to the rapidly growing field of genome biology. By studying the concepts and case studies presented, readers will gain significant insight and develop practical solutions for similar biological data mining projects in the future.

Product Details

ISBN:
9781420086843
Author:
Chen, Jake Y.
Publisher:
Chapman & Hall/CRC
Editor:
Lonardi, Stefano
Subject:
Database Management - Database Mining
Subject:
Data mining
Subject:
Bioinformatics
Subject:
General
Subject:
Computers-Reference - General
Series:
Chapman & Hall/CRC Data Mining and Knowledge Discovery
Publication Date:
20090931
Binding:
Hardcover
Language:
English
Illustrations:
Y
Pages:
713
Dimensions:
9.30x6.30x1.70 in. 2.50 lbs.

Related Subjects

Computers and Internet » Computers Reference » General
Science and Mathematics » Electricity » General Electronics

Biological Data Mining New Hardcover
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Product details 713 pages Chapman & Hall/CRC - English 9781420086843 Reviews:
"Synopsis" by , Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplinary data mining researchers who cover state-of-the-art biological topics.

The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput Omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications.

This volume examines the concepts, problems, progress, and trends in developing and applying new data mining techniques to the rapidly growing field of genome biology. By studying the concepts and case studies presented, readers will gain significant insight and develop practical solutions for similar biological data mining projects in the future.

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