Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Proceedings of the 11th international conference on World Wide Web
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of protein-protein interaction networks using random walks
Proceedings of the 5th international workshop on Bioinformatics
Disease gene prioritization based on topological similarity in protein-protein interaction networks
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Multi-label correlated semi-supervised learning for protein function prediction
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Transductive multi-label ensemble classification for protein function prediction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Protein Function Prediction using Multi-label Ensemble Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 0.00 |
We propose here a multi-label semi-supervised learning algorithm, PfunBG, to predict protein functions, employing a bi-relational graph (BG) of proteins and function annotations. Different from most, if not all, existing methods that only consider the partially labeled protein-protein interaction (PPI) network, the BG comprises three components, a PPI network, a function class graph induced from function annotations of such proteins, and a bipartite graph induced from function assignments. By referring to proteins and function classes equally as vertices, we exploit network propagation to measure how closely a specific function class is related to a protein of interest. The experiments on a yeast PPI network illustrate its effectiveness and efficiency.