Chapter 10. Evolutionary Granular Kernel Trees for Protein Subcellular Location Prediction

  1. Yan-Qing Zhang1 and
  2. Jagath C. Rajapakse2
  1. Bo Jin and
  2. Yan-Qing Zhang

Published Online: 21 APR 2008

DOI: 10.1002/9780470397428.ch10

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics

How to Cite

Jin, B. and Zhang, Y.-Q. (2008) Evolutionary Granular Kernel Trees for Protein Subcellular Location Prediction, in Machine Learning in Bioinformatics (eds Y.-Q. Zhang and J. C. Rajapakse), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470397428.ch10

Editor Information

  1. 1

    Georgia State University, Atlanta, Georgia

  2. 2

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

Author Information

  1. Georgia State University, Atlanta, Georgia

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:

  • evolutionary granular kernel trees;
  • SVMs with evolutionary granular kernel trees (EGKTs);
  • SVMs with EGKTs and one versus-one voting approach and protein subcellular location prediction

Summary

This chapter contains sections titled:

  • Introduction

  • Granular Feature Transformation and EGKTs

  • Multiclassification Approaches Based on SVMs Outputs

  • Simulations

  • Conclusions and Future Works

  • Acknowledgment

  • References