Bioinformatics Advance Access published online on September 17, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth491
Bioinformatics © Oxford University Press 2004; all rights reserved
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1 Institute for Scientific Interchange (ISI), Viale Settimio Severo 65, Turin, I-10133, Italy
* To whom correspondence should be addressed. E-mail: leone{at}isiosf.isi.it.
Motivations: 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 become that of correctly and reliably predicting the functions of a given protein with of functions exploiting information coming from the whole network of proteins physically interacting with the functionally undetermined one (Hodgman, 2000). In the present work we will refer to an "observed" protein as one present in protein-protein interaction networks published in the literature as i. e. in (Uetz and et al., 2000; Xenarios and et al., 2000; Giot and et al., 2003). Methods: The method proposed in this article is based on a message passing algorithm known as Belief Propagation (Yedidia et al., 2003), which takes as input the network of proteins physical interactions and a catalog of known proteins functions, and returns the probabilities for each unclassified protein of having one chosen function. The implementation of the algorithm allows for fast on-line analysis, and can be easily generalized to more complex graph topologies taking into account hyper-graphs, i.e. complexes of more than two interacting proteins. Results: Benchmarks of our method are two Saccharomyces cerevisiæ protein-protein interaction networks (PPI) as in (Uetz and et al., 2000) and in the Database of Interacting Proteins (DIP). The validity of our approach is successfully tested against other available techniques (Deng and et al., 2002; Letovsky and Kasif, 2003; Vazquez et al., 2003).
Revised July 1, 2004
Accepted August 16, 2004
Article
Predicting protein functions with message passing algorithms
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