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

Content Provider	IEEE Xplore Digital Library
Author	Shaik, J.S. Yeasin, M.
Copyright Year	2006
Description	Author affiliation: Computational Vision, Pattern and Image Analysis Lab, Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN – 38152 (Shaik, J.S.)
Abstract	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.
Starting Page	2972
Ending Page	2979
File Size	301389
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247253
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Robustness Algorithm design and analysis Data analysis Partitioning algorithms Computer vision Image analysis Performance analysis Biomedical imaging Diseases
Content Type	Text
Resource Type	Article

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