ClusFCM: an algorithm for predicting protein functions using homologies and protein interactions.
We introduce a new algorithm, called ClusFCM, which combines techniques of clustering and fuzzy cognitive maps (FCM) for prediction of protein functions. ClusFCM takes advantage of protein homologies and protein interaction network topology to improve low recall predictions associated with existing prediction methods. ClusFCM exploits the fact that proteins of known function tend to cluster together and deduce functions not only through their direct interaction with other proteins, but also from other proteins in the network. We use ClusFCM to annotate protein functions for Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), and Drosophila melanogaster (fly) using protein-protein interaction data from the General Repository for Interaction Datasets (GRID) database and functional labels from Gene Ontology (GO) terms. The algorithm's performance is compared with four state-of-the-art methods for function prediction--Majority, chi(2) statistics, Markov random field (MRF), and FunctionalFlow--using measures of Matthews correlation coefficient, harmonic mean, and area under the receiver operating characteristic (ROC) curves. The results indicate that ClusFCM predicts protein functions with high recall while not lowering precision. Supplementary information is available at http://www.egr.vcu.edu/cs/dmb/ClusFCM/.
Nguyen
Cao
C
Virginia Commonwealth University, VA 23238, USA. cdnguyen@vcu.edu
Mannino
Michael
M
Gardiner
Katheleen
K
Cios
Krzysztof J
KJ
D000465:Algorithms; D003198:Computer Simulation; D008954:Models, Biological; D005810:Multigene Family / Q000502:physiology*; D025941:Protein Interaction Mapping; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D015398:Signal Transduction / Q000502:physiology; D012984:Software; D013329:Structure-Activity Relationship
D020543:Proteome