Scalable pseudo-likelihood estimation in hybrid random fields
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable statistical learning: a modular Bayesian/Markov network approach
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Artificial Intelligence in Medicine
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Probabilistic graphical models have been developed in the past for the task of protein classification. In many cases, classifications obtained from the Gene Ontology have been used to validate these models. In this work we directly incorporate the structure of the Gene Ontology into the graphical representation for protein classification. We present a method in which each protein is represented by a replicate of the Gene Ontology structure, effectively modeling each protein in its own 'annotation space'. Proteins are also connected to one another according to different measures of functional similarity, after which belief propagation is run to make predictions at all ontology terms. Results: The proposed method was evaluated on a set of 4879 proteins from the Saccharomyces Genome Database whose interactions were also recorded in the GRID project. Results indicate that direct utilization of the Gene Ontology improves predictive ability, outperforming traditional models that do not take advantage of dependencies among functional terms. Average increase in accuracy (precision) of positive and negative term predictions of 27.8% (2.0%) over three different similarity measures and three subontologies was observed. Availability: C/C++/Perl implementation is available from authors upon request. Contact: vladimir@cs.rutgers.edu