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Data mining and applications in genomics

Author: Sio-Iong Ao
Publisher: [New York] : Springer Science + Business Media B.V., [2008 ©2008
Series: Lecture notes in electrical engineering, v. 25.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Topics  Read more...
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Sio-Iong Ao
ISBN: 9781402089749 1402089740
OCLC Number: 244421197
Description: xii, 152 pages : illustrations ; 24 cm.
Contents: Introduction --
Data mining algorithms --
Advances in genomic experiment techniques --
Case study I: hierarchical clustering and graph algorithms for Tag-SNP selection --
Case study II: constrained unidimensional scaling for linkage disequilibrium maps --
Case study II: hybrid PCA-NN algorithms for continuous microarray time series --
Discussion and future data mining projects.
Series Title: Lecture notes in electrical engineering, v. 25.
Responsibility: Sio-Iong Ao.
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Abstract:

This book provides a thorough introduction to the use of data mining algorithms as an investigative tool for applications in genomics. It then explores tremendous advances in the field and offers  Read more...

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    schema:reviewBody ""Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Topics covered include Genomic Techniques, Single Nucleotide Polymorphisms, Disease Studies, HapMap Project, Haplotypes, Tag-SNP Selection, Linkage Disequilibrium Map, Gene Regulatory Networks, Dimension Reduction, Feature Selection, Feature Extraction, Principal Component Analysis, Independent Component Analysis, Machine Learning Algorithms, Hybrid Intelligent Techniques, Clustering Algorithms, Graph Algorithms, Numerical Optimization Algorithms, Data Mining Software Comparison, Medical Case Studies, Bioinformatics Projects, and Medical Applications." "Data Mining and Applications in Genomics offers state of the art of tremendous advances in data mining algorithms and applications in genomics and also serve as an excellent reference work for researchers and graduate students working on data mining algorithms and applications in genomics."--Jacket." ;
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