Selecting informative genes from microarray dataset by incorporating gene ontology

Selecting informative genes from microarray experiments is one of the most important data analysis steps for deciphering biological information imbedded in such experiments. However, due to the characteristics of microarray technology and the underlying biology, namely large number of genes and limited number of samples, the statistical soundness of gene selection algorithm becomes questionable. One major problem is the high false discover rate. Microarray experiment is only one facet of current knowledge of the biological system under study. In this paper, we propose to alleviate this high false discover rate problem by integrating domain knowledge into the gene selection process. Gene ontology represents a controlled biological vocabulary and a repository of computable biological knowledge. It is shown in the literature that gene ontology-based similarities between genes carry significant information of the functional relationships. Integration of such domain knowledge into gene selection algorithms enables us to remove noisy genes intelligently. We propose an add-on algorithm applied to any single gene-based discriminative scores integrating domain knowledge from gene ontology annotation. Preliminary experiments are performed on publicly available colon cancer dataset to demonstrate the utility of the integration of domain knowledge for the purpose of gene selection. Our experiments show interesting results.

1. Introduction

Cancer classification traditionally relies upon morphological appearance of tumor. However, there has been increasing interest in classifying tumor molecularly with the advent of DNA microarray technologies for large-scale transcriptional profiling [13]. A typical DNA microarray study utilizes several DNA microarray chips on different tissue samples and generates a numerical array with thousands of rows (genes) and tens of columns (experiments/DNA chips). Sample classification can be performed by treating gene expression levels as features describing molecular states of the tissue in question. Various canned classification algorithms in the machine learning literature have been used for such classification task.