Biological pathways as features for microarray data classification

Authors:
Brian Quanz;Meeyoung Park;Jun Huan
Affiliations:
University of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA
Venue:
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
Year:
2008

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Abstract

Classification using microarray gene expression data is an important task in bioinformatics. Due to the high dimensionality and small sample size that characterizes microarray data, there has recently been a drive to incorporate any available information in addition to the expression data in the classification process. As a result, much work has begun on selecting biological pathways that are closely related to a clinical outcome of interest using the gene expression data, and incorporating this pathway information opens up new avenues for classification. As opposed to previous approaches that consider individual genes as features, we propose a new approach that treats biological pathways as features. Each pathway found to be significantly related to an outcome of interest is treated as a feature, and is mapped to a feature value. We define several methods for mapping pathways to features, and compare the performance of several classifiers using our feature transformations to that of the classifiers using individual genes as features for different feature selection methods.