dorsal/arxiv
View SchemaPredicting protein functions with message passing algorithms
| Authors | M. Leone, A. Pagnani |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0405007 |
| URL | https://arxiv.org/abs/q-bio/0405007 |
| DOI | 10.1093/bioinformatics/bth491 |
| Journal | Bioinformatics 21: 239-247 (2005). |
Abstract
Motivation: In the last few years a growing interest in biology has been shifting towards 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 observed but still functionally undetermined proteins starting from information coming from the network of co-observed proteins of known functions. Method: The method proposed in this article is based on a message passing algorithm known as Belief Propagation, 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, {\em i.e.} complexes of more than two interacting proteins.
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"abstract": "Motivation: In the last few years a growing interest in biology has been\nshifting towards the problem of optimal information extraction from the huge\namount of data generated via large scale and high-throughput techniques. One of\nthe most relevant issues has recently become that of correctly and reliably\npredicting the functions of observed but still functionally undetermined\nproteins starting from information coming from the network of co-observed\nproteins of known functions.\n Method: The method proposed in this article is based on a message passing\nalgorithm known as Belief Propagation, which takes as input the network of\nproteins physical interactions and a catalog of known proteins functions, and\nreturns the probabilities for each unclassified protein of having one chosen\nfunction. The implementation of the algorithm allows for fast on-line analysis,\nand can be easily generalized to more complex graph topologies taking into\naccount hyper-graphs, {\\em i.e.} complexes of more than two interacting\nproteins.",
"arxiv_id": "q-bio/0405007",
"authors": [
"M. Leone",
"A. Pagnani"
],
"categories": [
"q-bio.QM",
"cond-mat.dis-nn"
],
"doi": "10.1093/bioinformatics/bth491",
"journal_ref": "Bioinformatics 21: 239-247 (2005).",
"title": "Predicting protein functions with message passing algorithms",
"url": "https://arxiv.org/abs/q-bio/0405007"
},
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