7. Clustering Functionally Similar Genes from Microarray Data

  1. Pradipta Maji1 and
  2. Sankar K. Pal2

Published Online: 17 FEB 2012

DOI: 10.1002/9781118119723.ch7

Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging

Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging

How to Cite

Maji, P. and Pal, S. K. (2012) Clustering Functionally Similar Genes from Microarray Data, in Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118119723.ch7

Author Information

  1. 1

    Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

  2. 2

    Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India

Publication History

  1. Published Online: 17 FEB 2012
  2. Published Print: 27 JAN 2012

Book Series:

  1. Wiley Series on Bioinformatics: Computational Techniques and Engineering

Book Series Editors:

  1. Yi Pan and
  2. Albert Y. Zomaya

Series Editor Information

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

ISBN Information

Print ISBN: 9781118004401

Online ISBN: 9781118119723

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Keywords:

  • microarray gene expression data sets;
  • rough-fuzzy clustering algorithms

Summary

This chapter deals with the application of different rough-fuzzy clustering algorithms for clustering functionally similar genes from microarray gene expression data sets. The effectiveness of the algorithms, along with a comparison with other related gene clustering algorithms, is demonstrated on a set of microarray gene expression data sets using some standard validity indices. The chapter first reports a brief overview of different gene clustering algorithms. It then describes several quantitative and qualitative performance measures such as Silhouette index, Eisen and cluster profile plots, Z score, gene-ontology-based analysis to evaluate the quality of gene clusters. The chapter presents a brief description of different microarray gene expression data sets such as fifteen yeast data, yeast sporulation, Auble data, Cho et al. data, and reduced cell cycle data. It also presents implementation details, experimental results, and a comparison among different algorithms.

Controlled Vocabulary Terms

fuzzy set theory; pattern clustering; performance evaluation; rough set theory