Chapter 1. Feature Selection for Genomic and Proteomic Data Mining

  1. Yan-Qing Zhang3 and
  2. Jagath C. Rajapakse4
  1. Sun-Yuan Kung1 and
  2. Man-Wai Mak2

Published Online: 21 APR 2008

DOI: 10.1002/9780470397428.ch1

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics

How to Cite

Kung, S.-Y. and Mak, M.-W. (2008) Feature Selection for Genomic and Proteomic Data Mining, in Machine Learning in Bioinformatics (eds Y.-Q. Zhang and J. C. Rajapakse), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470397428.ch1

Editor Information

  1. 3

    Georgia State University, Atlanta, Georgia

  2. 4

    School of Computer Engineering, and The Bioinformatics Research Center, Nanyang Technological University, Nanyang, Singapore

Author Information

  1. 1

    Princeton University, Princeton, New Jersey, USA

  2. 2

    The Hong Kong Polytechnic University, Hong Kong, China

Publication History

  1. Published Online: 21 APR 2008
  2. Published Print: 12 NOV 2008

Book Series:

  1. Bioinformatics: Computational Techniques and Engineering

Book Series Editors:

  1. Professor Yi Pan and
  2. Professor Albert Y. Zomaya

ISBN Information

Print ISBN: 9780470116623

Online ISBN: 9780470397428

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Keywords:

  • extreme dimensionality in genomic data - serious concern in many applications;
  • feature selection for genomic and proteomic data mining;
  • feature selection via vector-index-adaptive SVM

Summary

This chapter contains sections titled:

  • Introduction

  • Quantifying Information/Redundancy of/among Features

  • Consecutive Ranking of Features

  • Supervised Feature Selection and Extraction

  • Feature Selection VIA Vector-Index-Adaptive SVM for Fast Subcellular Localization

  • Conclusion

  • Acknowledgments

  • References