PhenoPred is a web-based tool designed to detect novel gene-disease associations in humans. It is based on known gene-disease associations, protein-protein interaction data, protein functional annotation at a molecular level and protein sequence data. Machine learning principles are used to integrate heterogeneous data sources. PhenoPred can be used to prioritize genes based on their likelihood to be associated with a given disease or to prioritize diseases for a given query gene.

PhenoPred is based on HUGO Gene Nomenclature for gene names and Disease Ontology (DO) for the names of diseases. DO is based on International Classification of Diseases (ICD-9) maintained by the World Health Organization.
The PhenoPred web service was developed by Brandon Peters and Wyatt Clark. In citing the PhenoPred software, please refer to:

Radivojac P, Peng K, Clark WT, Peters BJ, Mohan A, Boyle SM, Mooney SD. An integrated approach to inferring gene-disease associations in humans. Proteins. 2008; 72(3):1030-1037. PubMed

Please direct all comments and suggestions to Predrag Radivojac.
Gentrepid by George et al. Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res. 2006;34(19):e130.PubMed
Endeavour by Aerts et al. Gene prioritization through genomic data fusion. Nat Biotechnol. 2006; 24(5):537-44.PubMed
TOM by Rossi et al. TOM: a web-based integrated approach for identification of candidate disease genes. Nucleic Acids Res. 2006; 34(Web Server issue):W285-92.PubMed
GeneSeeker by van Driel et al. GeneSeeker: extraction and integration of human disease-related information from web-based genetic databases. Nucleic Acids Res.2005;33(Web Server issue):W758-61.PubMed
PROSPECTR by Adie et al. Speeding disease gene discovery by sequence based candidate prioritization. BMC Bioinformatics. 2005; 6:55.PubMed
G2D by Perez-Iratxeta et al. G2D: A tool for Mining Genes Associated to Disease. BMC Genet. 2005; 6:45.PubMed
SVMPerf by Joachims. A support vector method for multivariate performance measures. Proceedings of the 22nd International Conference on Machine Learning, pp. 377-384, 2005.