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Conference Paper: Finding linear motif pairs from protein interaction networks: a probabilistic approach.

TitleFinding linear motif pairs from protein interaction networks: a probabilistic approach.
Authors
Issue Date2007
PublisherImperial College Press.
Citation
The 6th Annual International Conference on Computational Systems Bioinformatics (CSB), San Diego, CA., 13-17 August 2007. In Computational Systems Bioinformatics Conference Proceedings, 2007, v. 6, p. 111-119 How to Cite?
AbstractFinding motif pairs from a set of protein sequences based on the protein-protein interaction data is a challenging computational problem. Existing effective approaches usually rely on additional information such as some prior knowledge on protein groupings based on protein domains. In reality, this kind of knowledge is not always available. Novel approaches without using this knowledge is much desirable. Recently, Tan et al. proposed such an approach. However, there are two problems with their approach. The scoring function (using chi(2) testing) used in their approach is not adequate. Random motif pairs may have higher scores than the correct ones. Their approach is also not scalable. It may take days to process a set of 5000 protein sequences with about 20,000 interactions. In this paper, our contribution is two-fold. We first introduce a new scoring method, which is shown to be more accurate than the chi-score used in Tan et al. Then, we present two efficient algorithms, one exact algorithm and a heuristic version of it, to solve the problem of finding motif pairs. Based on experiments on real datasets, we show that our algorithms are efficient and can accurately locate the motif pairs. We have also evaluated the sensitivity and efficiency of our heuristics algorithm using simulated datasets, the results show that the algorithm is very efficient with reasonably high sensitivity.
Persistent Identifierhttp://hdl.handle.net/10722/93160
ISSN
2011 SCImago Journal Rankings: 0.309

 

DC FieldValueLanguage
dc.contributor.authorLeung, HCen_HK
dc.contributor.authorSiu, MHen_HK
dc.contributor.authorYiu, SMen_HK
dc.contributor.authorChin, FYen_HK
dc.contributor.authorSung, KWKen_HK
dc.date.accessioned2010-09-25T14:52:43Z-
dc.date.available2010-09-25T14:52:43Z-
dc.date.issued2007en_HK
dc.identifier.citationThe 6th Annual International Conference on Computational Systems Bioinformatics (CSB), San Diego, CA., 13-17 August 2007. In Computational Systems Bioinformatics Conference Proceedings, 2007, v. 6, p. 111-119en_HK
dc.identifier.issn1752-7791en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93160-
dc.description.abstractFinding motif pairs from a set of protein sequences based on the protein-protein interaction data is a challenging computational problem. Existing effective approaches usually rely on additional information such as some prior knowledge on protein groupings based on protein domains. In reality, this kind of knowledge is not always available. Novel approaches without using this knowledge is much desirable. Recently, Tan et al. proposed such an approach. However, there are two problems with their approach. The scoring function (using chi(2) testing) used in their approach is not adequate. Random motif pairs may have higher scores than the correct ones. Their approach is also not scalable. It may take days to process a set of 5000 protein sequences with about 20,000 interactions. In this paper, our contribution is two-fold. We first introduce a new scoring method, which is shown to be more accurate than the chi-score used in Tan et al. Then, we present two efficient algorithms, one exact algorithm and a heuristic version of it, to solve the problem of finding motif pairs. Based on experiments on real datasets, we show that our algorithms are efficient and can accurately locate the motif pairs. We have also evaluated the sensitivity and efficiency of our heuristics algorithm using simulated datasets, the results show that the algorithm is very efficient with reasonably high sensitivity.en_HK
dc.languageengen_HK
dc.publisherImperial College Press.-
dc.relation.ispartofComputational Systems Bioinformatics Conference Proceedingsen_HK
dc.subject.meshModels, Biological-
dc.subject.meshModels, Chemical-
dc.subject.meshProtein Interaction Mapping - methods-
dc.subject.meshProteins - chemistry - metabolism-
dc.subject.meshSequence Analysis, Protein - methods-
dc.titleFinding linear motif pairs from protein interaction networks: a probabilistic approach.en_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1752-7791&volume=6&spage=111&epage=119&date=2007&atitle=Finding+linear+motif+pairs+from+protein+interaction+networks:+a+probabilistic+approach-
dc.identifier.emailLeung, HC:cmleung2@cs.hku.hken_HK
dc.identifier.emailYiu, SM:smyiu@cs.hku.hken_HK
dc.identifier.emailChin, FY:chin@cs.hku.hken_HK
dc.identifier.authorityLeung, HC=rp00144en_HK
dc.identifier.authorityYiu, SM=rp00207en_HK
dc.identifier.authorityChin, FY=rp00105en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.pmid17951817-
dc.identifier.scopuseid_2-s2.0-38449105308en_HK
dc.identifier.hkuros161356en_HK
dc.identifier.volume6en_HK
dc.identifier.spage111en_HK
dc.identifier.epage119en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.description.otherThe 6th Annual International Conference on Computational Systems Bioinformatics (CSB), San Diego, CA., 13-17 August 2007. In Computational Systems Bioinformatics Conference Proceedings, 2007, v. 6, p. 111-119-
dc.identifier.scopusauthoridLeung, HC=35233742700en_HK
dc.identifier.scopusauthoridSiu, MH=36762173800en_HK
dc.identifier.scopusauthoridYiu, SM=7003282240en_HK
dc.identifier.scopusauthoridChin, FY=7005101915en_HK
dc.identifier.scopusauthoridSung, KWK=12797768900en_HK

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