Gene Classification using Expression Profiles: A Feasibility Study

As various genome sequencing projects have already been completed or are near completion, genome researchers are shifting their focus from structural genomics to functional genomics. Functional genomics represents the next phase, that expands the biological investigation to studying the functionality of genes of a single organism as well as studying and correlating the functionality of genes across many different organisms. Recently developed methods for monitoring genome-wide mRNA expression changes hold the promise of allowing us to inexpensively gain insights into the function of unknown genes. In this paper we focus on evaluating the feasibility of using supervised machine learning methods for determining the function of genes based solely on their expression profiles. We experimentally evaluate the performance of traditional classification algorithms such as support vector machines and k-nearest neighbors on the yeast genome, and present new approaches for classification that imp...

• Type: Conference
• Year: 2001
• Where: BIBE
• Authors: Michihiro Kuramochi, George Karypis
• Keywords: BIBE 2001, Bioinformatics, Expression Profiles, Functional Genomics, Unknown Genes