title: PROSNET: INTEGRATING HOMOLOGY WITH MOLECULAR NETWORKS FOR PROTEIN FUNCTION PREDICTION.
abstract: Automated annotation of protein function has become a critical task in the post-genomic era. Network-based approaches and homology-based approaches have been widely used and recently tested in large-scale community-wide assessment experiments. It is natural to integrate network data with homology information to further improve the predictive performance. However, integrating these two heterogeneous, high-dimensional and noisy datasets is non-trivial. In this work, we introduce a novel protein function prediction algorithm ProSNet. An integrated heterogeneous network is first built to include molecular networks of multiple species and link together homologous proteins across multiple species. Based on this integrated network, a dimensionality reduction algorithm is introduced to obtain compact low-dimensional vectors to encode proteins in the network. Finally, we develop machine learning classification algorithms that take the vectors as input and make predictions by transferring annotations both within each species and across different species. Extensive experiments on five major species demonstrate that our integration of homology with molecular networks substantially improves the predictive performance over existing approaches.
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
authors.forename: Sheng
authors.initials: S
authors.lastname: Wang
authors.forename: Meng
authors.initials: M
authors.lastname: Qu
authors.forename: Jian
authors.initials: J
authors.lastname: Peng
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D006801:Humans; D000069550:Machine Learning; D051379:Mice; D058977:Molecular Sequence Annotation / Q000706:statistics & numerical data*; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D017386:Sequence Homology, Amino Acid