Probabilistic paths for protein complex inference
RECOMB'06 Proceedings of the joint 2006 satellite conference on Systems biology and computational proteomics
Resource Allocation via Message Passing
INFORMS Journal on Computing
Towards an integrated protein-protein interaction network
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Resource Allocation via Message Passing
INFORMS Journal on Computing
| Hi-index | 3.84 |
Motivation: 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. Methods: 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. Results: Benchmarks of our method are the two Saccharomyces cerevisiae protein--protein interaction networks and the Database of Interacting Proteins. The validity of our approach is successfully tested against other available techniques. Contact: leone@isiosf.isi.it Supplementary information: http://isiosf.isi.it/~pagnani