Bayesian Markov Random Field analysis for protein function prediction based on network data.

ABSTRACT: 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-prot

SUBMITTER: Kourmpetis YA 

PROVIDER: S-EPMC2827541 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature