Bayesian Markov Random Field Analysis for Protein Function PredictionBased on Network Data

摘要

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S.cereviciae validation networkwith 1622 proteins against 90 Gene Ontology terms of different levels ofabstraction. Compared to three other protein function prediction methods, ourapproach shows very good prediction performance. Our method can be directlyapplied to protein-protein interaction or coexpression networks, but also can beextended to use multiple data sources. We apply our method to physical proteininteraction data from S. cerevisiae and provide novelpredictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and weevaluate the predictions using the available literature.