Data Mining and Applications in Genomics (Lecture Notes in Electrical Engineering, 25)

Ao, Sio-Iong

ISBN 10: 1402089740 / ISBN 13: 9781402089749

Published by Springer, 2008

About this Item

Clean text, crisp pages. Very good overall condition. Ships next business day from NC.

Seller Inventory # STBXA7-041322-11.2M-VG-BSTK-89.95-012

Bibliographic Details

• Title: Data Mining and Applications in Genomics (Lecture Notes in Electrical Engineering, 25)
• Publisher: Springer
• Publication Date: 2008
• Language: English
• ISBN 10: 1402089740
• ISBN 13: 9781402089749
• Binding: Hardcover
• Condition: Very Good

About this title

Synopsis

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. Data Mining and Applications in Genomics offers state of the art of tremendous advances in data mining algorithms and applications in genomics and also serves as an excellent reference work for researchers and graduate students working on data mining algorithms and applications in genomics.

From the Back Cover

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.