Software note: Hepatitis C virus contact map prediction based on binary encoding strategy
Computational Biology and Chemistry
Protein function prediction with the shortest path in functional linkage graph and boosting
International Journal of Bioinformatics Research and Applications
Molecular Function Prediction Using Neighborhood Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Protein function prediction based on patterns in biological networks
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Artificial Intelligence in Medicine
Metric labeling and semi-metric embedding for protein annotation prediction
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
DTP: decision tree-based predictor of protein contact map
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Conditional random fields for protein function prediction
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
| Hi-index | 3.84 |
Motivation: Gene Ontology (GO) consortium provides structural description of protein function that is used as a common language for gene annotation in many organisms. Large-scale techniques have generated many valuable protein--protein interaction datasets that are useful for the study of protein function. Combining both GO and protein--protein interaction data allows the prediction of function for unknown proteins. Result: We apply a Markov random field method to the prediction of yeast protein function based on multiple protein--protein interaction datasets. We assign function to unknown proteins with a probability representing the confidence of this prediction. The functions are based on three general categories of cellular component, molecular function and biological process defined in GO. The yeast proteins are defined in the Saccharomyces Genome Database (SGD). The protein--protein interaction datasets are obtained from the Munich Information Center for Protein Sequences (MIPS), including physical interactions and genetic interactions. The efficiency of our prediction is measured by applying the leave-one-out validation procedure to a functional path matching scheme, which compares the prediction with the GO description of a protein's function from the abstract level to the detailed level along the GO structure. For biological process, the leave-one-out validation procedure shows 52% precision and recall of our method, much better than that of the simple guilty-by-association methods. Supplementary material: http://www.cmb.usc.edu/~msms/gomapping