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.
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.
Authors: Kasper Lage; E Olof Karlberg; Zenia M Størling; Páll I Olason; Anders G Pedersen; Olga Rigina; Anders M Hinsby; Zeynep Tümer; Flemming Pociot; Niels Tommerup; Yves Moreau; Søren Brunak Journal: Nat Biotechnol Date: 2007-03 Impact factor: 54.908
Authors: Andrew I Su; Michael P Cooke; Keith A Ching; Yaron Hakak; John R Walker; Tim Wiltshire; Anthony P Orth; Raquel G Vega; Lisa M Sapinoso; Aziz Moqrich; Ardem Patapoutian; Garret M Hampton; Peter G Schultz; John B Hogenesch Journal: Proc Natl Acad Sci U S A Date: 2002-03-19 Impact factor: 11.205
Authors: Euan A Adie; Richard R Adams; Kathryn L Evans; David J Porteous; Ben S Pickard Journal: BMC Bioinformatics Date: 2005-03-14 Impact factor: 3.169
Authors: U Martin Singh-Blom; Nagarajan Natarajan; Ambuj Tewari; John O Woods; Inderjit S Dhillon; Edward M Marcotte Journal: PLoS One Date: 2013-05-01 Impact factor: 3.240