Integrating gene ontology into discriminative powers of genes for feature selection in microarray data

Authors:
Jianlong Qi;Jian Tang
Affiliations:
Memorial University of Newfoundland, Canada;Memorial University of Newfoundland, Canada
Venue:
Proceedings of the 2007 ACM symposium on Applied computing
Year:
2007

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Abstract

One of the main challenges in the classification of microarray gene expression data is the small sample size compared with the large number of genes, so feature selection is an essential step to remove genes not relevant to class labels. Most feature selection methods are solely based on expression values to determine discriminative values of genes and remove redundancy. However, due to the characteristics of microarray technology, some values may not be accurately measured. This may reduce the effectiveness of these models. To cope with this problem, in this paper, we integrate Gene Ontology (GO) annotations into gene selection. The novelty of our work is to evaluate genes based on not only their individual discriminative powers but also the powers of GO terms that annotate them. This strategy implicitly verifies the accuracies of the measurements and reduces redundancy. Experimental results in four public datasets demonstrate the effectiveness of the proposed method.