pubmed-article:15877074 | rdf:type | pubmed:Citation | lld:pubmed |
pubmed-article:15877074 | lifeskim:mentions | umls-concept:C1882071 | lld:lifeskim |
pubmed-article:15877074 | lifeskim:mentions | umls-concept:C0459471 | lld:lifeskim |
pubmed-article:15877074 | lifeskim:mentions | umls-concept:C0033684 | lld:lifeskim |
pubmed-article:15877074 | lifeskim:mentions | umls-concept:C1704675 | lld:lifeskim |
pubmed-article:15877074 | lifeskim:mentions | umls-concept:C0314603 | lld:lifeskim |
pubmed-article:15877074 | lifeskim:mentions | umls-concept:C0220922 | lld:lifeskim |
pubmed-article:15877074 | pubmed:issue | 5 | lld:pubmed |
pubmed-article:15877074 | pubmed:dateCreated | 2005-5-6 | lld:pubmed |
pubmed-article:15877074 | pubmed:abstractText | Genetic interaction analysis,in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between- or within-pathway explanations encoded in the physical networks, covering approximately 40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than within-pathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function. | lld:pubmed |
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pubmed-article:15877074 | pubmed:language | eng | lld:pubmed |
pubmed-article:15877074 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
pubmed-article:15877074 | pubmed:citationSubset | IM | lld:pubmed |
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pubmed-article:15877074 | pubmed:status | MEDLINE | lld:pubmed |
pubmed-article:15877074 | pubmed:month | May | lld:pubmed |
pubmed-article:15877074 | pubmed:issn | 1087-0156 | lld:pubmed |
pubmed-article:15877074 | pubmed:author | pubmed-author:IdekerTreyT | lld:pubmed |
pubmed-article:15877074 | pubmed:author | pubmed-author:KelleyRyanR | lld:pubmed |
pubmed-article:15877074 | pubmed:issnType | Print | lld:pubmed |
pubmed-article:15877074 | pubmed:volume | 23 | lld:pubmed |
pubmed-article:15877074 | pubmed:owner | NLM | lld:pubmed |
pubmed-article:15877074 | pubmed:authorsComplete | Y | lld:pubmed |
pubmed-article:15877074 | pubmed:pagination | 561-6 | lld:pubmed |
pubmed-article:15877074 | pubmed:dateRevised | 2010-12-17 | lld:pubmed |
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pubmed-article:15877074 | pubmed:year | 2005 | lld:pubmed |
pubmed-article:15877074 | pubmed:articleTitle | Systematic interpretation of genetic interactions using protein networks. | lld:pubmed |
pubmed-article:15877074 | pubmed:affiliation | Program in Bioinformatics, University of California, San Diego, 9500 Gilman Dr., San Diego, California 92093-0412, USA. | lld:pubmed |
pubmed-article:15877074 | pubmed:publicationType | Journal Article | lld:pubmed |
pubmed-article:15877074 | pubmed:publicationType | Research Support, U.S. Gov't, P.H.S. | lld:pubmed |
pubmed-article:15877074 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
pubmed-article:15877074 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
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