This article is part of the supplement: Quantitative inference of gene function from diverse large-scale datasets

Research

Predicting gene function in a hierarchical context with an ensemble of classifiers

Yuanfang Guan^1,2, Chad L Myers^2,3, David C Hess², Zafer Barutcuoglu³, Amy A Caudy² and Olga G Troyanskaya^2,3

¹Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA

²Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA

³Department of Computer Science, Princeton University, Princeton, NJ 08544, USA

author email corresponding author email

Genome Biology 2008, 9(Suppl 1):S3doi:10.1186/gb-2008-9-s1-s3

Published:	27 June 2008

Abstract

Background:

The wide availability of genome-scale data for several organisms has stimulated interest in computational approaches to gene function prediction. Diverse machine learning methods have been applied to unicellular organisms with some success, but few have been extensively tested on higher level, multicellular organisms. A recent mouse function prediction project (MouseFunc) brought together nine bioinformatics teams applying a diverse array of methodologies to mount the first large-scale effort to predict gene function in the laboratory mouse.

Results:

In this paper, we describe our contribution to this project, an ensemble framework based on the support vector machine that integrates diverse datasets in the context of the Gene Ontology hierarchy. We carry out a detailed analysis of the performance of our ensemble and provide insights into which methods work best under a variety of prediction scenarios. In addition, we applied our method to Saccharomyces cerevisiae and have experimentally confirmed functions for a novel mitochondrial protein.

Conclusion:

Our method consistently performs among the top methods in the MouseFunc evaluation. Furthermore, it exhibits good classification performance across a variety of cellular processes and functions in both a multicellular organism and a unicellular organism, indicating its ability to discover novel biology in diverse settings.