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Performance Evaluation of Subspace-based Algorithm in Selecting Differentially Expressed Genes and Classification of Tissue Types from Microarray Data

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2 Author(s)
Shaik, J.S. ; Computational Vision, Pattern and Image Analysis Lab, Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN – 38152 ; Yeasin, M.

This paper presents the implementation and evaluation of subspace-based clustering algorithm for robust selection of differentially expressed genes as well as the classification of tissue types from microarray data. The performance of the proposed algorithm is compared against other well known clustering algorithms and the quality of clusters is evaluated using a number of cluster validation indices. Empirical analyses on a number of synthetic and real microarray data sets suggest that the proposed subspace-based algorithm is robust in selecting differentially expressed genes and performs significantly better compared to popular clustering algorithms in selecting differentially expressed genes and classifying different tissue types.

Published in:

Neural Networks, 2006. IJCNN '06. International Joint Conference on

Date of Conference:

16-21 July 2006