PMC:4331678 / 5642-6605 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331678","sourcedb":"PMC","sourceid":"4331678","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331678","text":"To overcome the limitations of existing techniques, we introduce a new approach to integrate Multiple Networks (MNet) for prediction of protein functions. Unlike the aforementioned methods, MNet jointly optimizes the multiple network integration and the network-based classifier for a set of function labels in a unified objective function. In addition, MNet takes into account the unbalanced label problem in protein function prediction, and incorporates a label weighted scheme into the unified objective function to give more emphasis to the functional labels with fewer proteins. Our empirical study on four publicly available species (yeast, human, fly, and mouse, with different number of individual networks), annotated with thousands of GO terms, shows that MNet performs better (according to different evaluation criteria) than other related techniques. Furthermore, MNet, unlike the competitive methods, enables an easy selection of suitable parameters.","tracks":[]}