Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Research article

ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

Fantine Mordelet1234 and Jean-Philippe Vert123*

Author Affiliations

1 Centre for Computational Biology, Mines ParisTech, Fontainebleau,F-77300 France

2 Institut Curie, Paris, F-75248 France

3 U900, INSERM, Paris, F-75248 France

4 CREST, INSEE, Malakoff, F-92240 France

For all author emails, please log on.

BMC Bioinformatics 2011, 12:389  doi:10.1186/1471-2105-12-389

Published: 6 October 2011

Abstract

Background

Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases.

Results

We propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases.

Conclusions

ProDiGe implements a new machine learning paradigm for gene prioritization, which could help the identification of new disease genes. It is freely available at http://cbio.ensmp.fr/prodige webcite.