Source:http://linkedlifedata.com/resource/pubmed/id/14555619
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
15
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| pubmed:dateCreated |
2003-10-13
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| pubmed:abstractText |
In order to understand the molecular machinery of the cell, we need to know about the multitude of protein-protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein-protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction-repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction-repulsion method performs better than several competing techniques.
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| pubmed:grant | |
| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:chemical | |
| pubmed:status |
MEDLINE
|
| pubmed:month |
Oct
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| pubmed:issn |
1367-4803
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| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:day |
12
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| pubmed:volume |
19
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
1875-81
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| pubmed:dateRevised |
2007-11-14
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| pubmed:meshHeading |
pubmed-meshheading:14555619-Algorithms,
pubmed-meshheading:14555619-Amino Acid Sequence,
pubmed-meshheading:14555619-Artificial Intelligence,
pubmed-meshheading:14555619-Binding Sites,
pubmed-meshheading:14555619-Computer Simulation,
pubmed-meshheading:14555619-Fungal Proteins,
pubmed-meshheading:14555619-Models, Biological,
pubmed-meshheading:14555619-Models, Chemical,
pubmed-meshheading:14555619-Molecular Sequence Data,
pubmed-meshheading:14555619-Pattern Recognition, Automated,
pubmed-meshheading:14555619-Protein Binding,
pubmed-meshheading:14555619-Protein Interaction Mapping,
pubmed-meshheading:14555619-Proteins,
pubmed-meshheading:14555619-Reproducibility of Results,
pubmed-meshheading:14555619-Sensitivity and Specificity,
pubmed-meshheading:14555619-Sequence Alignment,
pubmed-meshheading:14555619-Sequence Analysis, Protein,
pubmed-meshheading:14555619-Structure-Activity Relationship
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| pubmed:year |
2003
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| pubmed:articleTitle |
Learning to predict protein-protein interactions from protein sequences.
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| pubmed:affiliation |
Unité de Biochimie et Biologie Moléculaire des Insectes, Institut Pasteur, 75724 Paris Cedex 15, France. sgomez@pasteur.fr
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| pubmed:publicationType |
Journal Article,
Comparative Study,
Research Support, U.S. Gov't, P.H.S.,
Research Support, U.S. Gov't, Non-P.H.S.,
Research Support, Non-U.S. Gov't,
Evaluation Studies,
Validation Studies
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