A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge

Background: The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage. Results: Protein-protein interaction data can be integrated with the functional knowledge existing in the Gene Ontology (GO) database. We apply similarity measures to assess the functional similarity between interacting proteins. We present a probabilistic framework for predicting functions of unknown proteins based on the functional similarity. We use ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Young-Rae Cho, Lei Shi, Murali Ramanathan, Aidong Zhang
• Keywords: BMCBI 2008, Interaction Data, Protein-protein Interactions, Proteins