Data Mining and Applications in Genomics

Sio-Iong Ao (auth.)1402089740, 978-1-4020-8974-9, 978-1-4020-8975-6

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.

Table of contents :
Front Matter….Pages i-xii
Introduction….Pages 1-13
Data Mining Algorithms….Pages 15-38
Advances in Genomic Experiment Techniques….Pages 39-52
Case Study I: Hierarchical Clustering and Graph Algorithms for Tag-SNP Selection….Pages 53-71
Case Study II: Constrained Unidimensional Scaling for Linkage Disequilibrium Maps….Pages 73-115
Case Study III: Hybrid PCA-NN Algorithms for Continuous Microarray Time Series….Pages 117-129
Discussions and Future Data Mining Projects….Pages 131-139
Back Matter….Pages 141-152