Data Mining and Applications in Genomics

  • Sio-Iong Ao

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 25)

About this book

Introduction

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.

Keywords

Haplotype SNP Single Nucleotide Polymorphism algorithms bioinformatics biology clustering computational biology data mining development genome genomics machine learning microarray network

Authors and affiliations

  • Sio-Iong Ao
    • 1
  1. 1.International Association of EngineersOxford UniversityUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4020-8975-6
  • Copyright Information Springer Science+Business Media B.V. 2008
  • Publisher Name Springer, Dordrecht
  • eBook Packages Computer Science
  • Print ISBN 978-1-4020-8974-9
  • Online ISBN 978-1-4020-8975-6
  • Series Print ISSN 1876-1100
  • About this book