Predicting protein functions with message passing algorithms

Bioinformatics
Michele Leone, Andrea Pagnani

Abstract

In the last few years, a growing interest in biology has been shifting toward the problem of optimal information extraction from the huge amount of data generated via large-scale and high-throughput techniques. One of the most relevant issues has recently emerged that of correctly and reliably predicting the functions of a given protein with that of functions exploiting information coming from the whole network of proteins physically interacting with the functionally undetermined one. In the present work, we will refer to an 'observed' protein as the one present in the protein-protein interaction networks published in the literature. The method proposed in this paper is based on a message passing algorithm known as Belief Propagation, which accepts the network of protein's physical interactions and a catalog of known protein's functions as input, and returns the probabilities for each unclassified protein of having one chosen function. The implementation of the algorithm allows for fast online analysis, and can easily be generalized into more complex graph topologies taking into account hypergraphs, i.e. complexes of more than two interacting proteins. Benchmarks of our method are the two Saccharomyces cerevisiae protein-protei...Continue Reading

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Citations

Apr 7, 2006·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Ariel JaimovichNir Friedman
Jul 20, 2006·Journal of the Royal Society, Interface·Petter Holme, Mikael Huss
Apr 3, 2008·Genome Research·Trey Ideker, Roded Sharan
Mar 3, 2006·Genome Research·Sourav BandyopadhyayTrey Ideker
May 29, 2008·BMC Bioinformatics·Jordi EspadalerBaldomero Oliva
Jul 22, 2008·Genome Biology·Lourdes Peña-CastilloFrederick P Roth
Jul 3, 2009·Proceedings of the National Academy of Sciences of the United States of America·Ginestra BianconiMatteo Marsili
Jun 10, 2006·Briefings in Bioinformatics·Pedro LarrañagaVictor Robles
Mar 5, 2020·Bioinformatics·Renming LiuArjun Krishnan

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