BACKGROUND: Now multiple types of data are available for identifying disease genes. Those data include gene-disease associations, disease phenotype similarities, protein-protein interactions, pathways, gene expression profiles, etc.. It is believed that integrating different kinds of biological data is an effective method to identify disease genes. RESULTS: In this paper, we propose a multiple data integration method based on the theory of Markov random field (MRF) and the method of Bayesian analysis for identifying human disease genes. The proposed method is not only flexible in easily incorporating different kinds of data, but also reliable in predicting candidate disease genes. CONCLUSIONS: Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. Predictions are evaluated by the leave-one-out method. The proposed method achieves an AUC score of 0.743 when integrating all those biological data in our experiments.
BACKGROUND: Now multiple types of data are available for identifying disease genes. Those data include gene-disease associations, disease phenotype similarities, protein-protein interactions, pathways, gene expression profiles, etc.. It is believed that integrating different kinds of biological data is an effective method to identify disease genes. RESULTS: In this paper, we propose a multiple data integration method based on the theory of Markov random field (MRF) and the method of Bayesian analysis for identifying human disease genes. The proposed method is not only flexible in easily incorporating different kinds of data, but also reliable in predicting candidate disease genes. CONCLUSIONS: Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. Predictions are evaluated by the leave-one-out method. The proposed method achieves an AUC score of 0.743 when integrating all those biological data in our experiments.
Authors: Jean-François Rual; Kavitha Venkatesan; Tong Hao; Tomoko Hirozane-Kishikawa; Amélie Dricot; Ning Li; Gabriel F Berriz; Francis D Gibbons; Matija Dreze; Nono Ayivi-Guedehoussou; Niels Klitgord; Christophe Simon; Mike Boxem; Stuart Milstein; Jennifer Rosenberg; Debra S Goldberg; Lan V Zhang; Sharyl L Wong; Giovanni Franklin; Siming Li; Joanna S Albala; Janghoo Lim; Carlene Fraughton; Estelle Llamosas; Sebiha Cevik; Camille Bex; Philippe Lamesch; Robert S Sikorski; Jean Vandenhaute; Huda Y Zoghbi; Alex Smolyar; Stephanie Bosak; Reynaldo Sequerra; Lynn Doucette-Stamm; Michael E Cusick; David E Hill; Frederick P Roth; Marc Vidal Journal: Nature Date: 2005-09-28 Impact factor: 49.962
Authors: Ulrich Stelzl; Uwe Worm; Maciej Lalowski; Christian Haenig; Felix H Brembeck; Heike Goehler; Martin Stroedicke; Martina Zenkner; Anke Schoenherr; Susanne Koeppen; Jan Timm; Sascha Mintzlaff; Claudia Abraham; Nicole Bock; Silvia Kietzmann; Astrid Goedde; Engin Toksöz; Anja Droege; Sylvia Krobitsch; Bernhard Korn; Walter Birchmeier; Hans Lehrach; Erich E Wanker Journal: Cell Date: 2005-09-23 Impact factor: 41.582
Authors: Andrew I Su; Tim Wiltshire; Serge Batalov; Hilmar Lapp; Keith A Ching; David Block; Jie Zhang; Richard Soden; Mimi Hayakawa; Gabriel Kreiman; Michael P Cooke; John R Walker; John B Hogenesch Journal: Proc Natl Acad Sci U S A Date: 2004-04-09 Impact factor: 11.205
Authors: S Kerrien; Y Alam-Faruque; B Aranda; I Bancarz; A Bridge; C Derow; E Dimmer; M Feuermann; A Friedrichsen; R Huntley; C Kohler; J Khadake; C Leroy; A Liban; C Lieftink; L Montecchi-Palazzi; S Orchard; J Risse; K Robbe; B Roechert; D Thorneycroft; Y Zhang; R Apweiler; H Hermjakob Journal: Nucleic Acids Res Date: 2006-12-01 Impact factor: 16.971