Integration of Relational and Hierarchical Network Information for Protein Function Prediction

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dc.contributor.author Jiang, Xiaoyu en_US
dc.contributor.author Nariai, Naoki en_US
dc.contributor.author Steffen, Martin en_US
dc.contributor.author Kasif, Simon en_US
dc.contributor.author Kolaczyk, Eric D en_US
dc.date.accessioned 2012-01-11T00:37:40Z
dc.date.available 2012-01-11T00:37:40Z
dc.date.copyright 2008 en_US
dc.date.issued 2008-8-22 en_US
dc.identifier.citation Jiang, Xiaoyu, Naoki Nariai, Martin Steffen, Simon Kasif, Eric D Kolaczyk. "Integration of relational and hierarchical network information for protein function prediction" BMC Bioinformatics 9:350. (2008) en_US
dc.identifier.issn 1471-2105 en_US
dc.identifier.uri http://hdl.handle.net/2144/3004
dc.description.abstract BACKGROUND. In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. RESULTS. We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing. CONCLUSION. A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods. en_US
dc.description.sponsorship National Human Genome Research Institute (R01 HG003367-01A1); National Institutes of Health (GM078987); National Science Foundation (ITR-048715); Office of Naval Research (N00014-06-1-0096) en_US
dc.language.iso en en_US
dc.publisher BioMed Central en_US
dc.rights Copyright 2008 Jiang et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en_US
dc.rights.uri http://creativecommons.org/licenses/by/2.0 en_US
dc.title Integration of Relational and Hierarchical Network Information for Protein Function Prediction en_US
dc.type article en_US
dc.identifier.doi 10.1186/1471-2105-9-350 en_US
dc.identifier.pubmedid 18721473 en_US
dc.identifier.pmcid 2535605 en_US

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