Finding minimal sets of informative genes in microarray data

Authors:
Kung-Hua Chang;Yong Kyun Kwon;D. Stott Parker
Affiliations:
Department of Computer Science, University of California, Los Angeles, Los Angeles, CA;Department of Computer Science, University of California, Los Angeles, Los Angeles, CA;Department of Computer Science, University of California, Los Angeles, Los Angeles, CA
Venue:
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Year:
2007

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Extraction of informative genes from microarray data

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation

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Abstract

For a microarray dataset with attached phenotype information - which gives expression levels of various genes and a phenotype classification for each of a set of samples - an important problem is to find informative genes. These genes have high information content as attributes for classification, minimizing the expected number of tests needed to identify a phenotype. This study investigates the use of a heuristic method for finding complete sets of informative genes (sets that are sufficient for constructing a maximally discriminating classifier) that are as small as possible. These minimal sets of informative genes can be very useful in developing an appreciation for the data. Our method uses branch-and-bound depth-first search. Experimental results suggest that our method is effective in finding minimal gene sets, and the resulting classifiers have good performance in terms of classification accuracy.