CombFunc: predicting protein function using heterogeneous data sources.
Only a small fraction of known proteins have been functionally characterized, making protein function prediction essential to propose annotations for uncharacterized proteins. In recent years many function prediction methods have been developed using various sources of biological data from protein sequence and structure to gene expression data. Here we present the CombFunc web server, which makes Gene Ontology (GO)-based protein function predictions. CombFunc incorporates ConFunc, our existing function prediction method, with other approaches for function prediction that use protein sequence, gene expression and protein-protein interaction data. In benchmarking on a set of 1686 proteins CombFunc obtains precision and recall of 0.71 and 0.64 respectively for gene ontology molecular function terms. For biological process GO terms precision of 0.74 and recall of 0.41 is obtained. CombFunc is available at http://www.sbg.bio.ic.ac.uk/combfunc.
Centre for Bioinformatics, Imperial College London, London, SW7 2AZ, UK. mark.wass04@imperial.ac.uk
Mark N
MN
Wass
Geraint
G
Barton
Michael J E
MJ
Sternberg
D000465:Algorithms; D015870:Gene Expression; D020407:Internet; D060066:Protein Interaction Maps; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000502:physiology*; D020539:Sequence Analysis, Protein; D012984:Software
D011506:Proteins