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Bioinformatics Advance Access published online on August 23, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti618
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received August 13, 2004
Revised August 2, 2005
Accepted August 8, 2005

Article

Analysis of recursive gene selection approaches from micro-array data

Fan Li 1* and Yiming Yang 1

1 Language Technology Institute, 4502 NSH., Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213

* To whom correspondence should be addressed.
Fan Li, E-mail: hustlf{at}cs.cmu.edu


  Abstract

Motivation: Finding a small subset of most predictive genes from microarray for disease prediction is a challenging problem. Support Vector Machines (SVMs) have been found successful with a recursive procedure in selecting important genes for cancer prediction. It is not well understood, however, how much the success depends on the choice of the specific classifier, and how much on the recursive procedure. We answer this question by examining multiple classifers (SVM, ridge regression and Rocchio) with feature selection in recursive and non-recursive settings on three DNA micro-array datasets (ALL-AML Leukemia data, Breast Cancer data and GCM data).

Results: We found recursive ridge regression most effective. On the AML-ALL dataset, it achieved zero error rate on the test set using only 3 genes (selected from over 7000), which is more encouraging than the best published result (zero error rate using 8 genes by recursive SVM). On the Breast Cancer dataset and the two largest categories of the GCM dataset, the results achieved by recursive ridge regression are also very encouraging. A further analysis of the experimental results shows that different classifiers penalize redundant features to different extent and this property plays an important role in the recursive feature selection process. Ridge regression classifier tends to penalize redundant features to a much larger extent than the SVM does. This may be the reason that recursive ridge regression has a better performance in selecting genes.

Availability: The data sets are available in http://sdmc.lit.org.sg:8080/GEDatasets/Datasets.html.


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