Prediction of extracellular matrix proteins based on distinctive sequence and domain characteristics

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dc.contributor.authorJ Jung-
dc.contributor.authorTaewoo Ryu-
dc.contributor.authorY Hwang-
dc.contributor.authorE Lee-
dc.contributor.authorD Lee-
dc.date.accessioned2017-04-19T09:17:32Z-
dc.date.available2017-04-19T09:17:32Z-
dc.date.issued2010-
dc.identifier.issn1066-5277-
dc.identifier.uri10.1089/cmb.2008.0236ko
dc.identifier.urihttps://oak.kribb.re.kr/handle/201005/9448-
dc.description.abstractExtracellular matrix (ECM) proteins are secreted to the exterior of the cell, and function as mediators between resident cells and the external environment. These proteins not only support cellular structure but also participate in diverse processes, including growth, hormonal response, homeostasis, and disease progression. Despite their importance, current knowledge of the number and functions of ECM proteins is limited. Here, we propose a computational method to predict ECM proteins. Specific features, such as ECM domain score and repetitive residues, were utilized for prediction. Based on previously employed and newly generated features, discriminatory characteristics for ECM protein categorization were determined, which significantly improved the performance of Random Forest and support vector machine (SVM) classification. We additionally predicted novel ECM proteins from non-annotated human proteins, validated with gene ontology and earlier literature. Our novel prediction method is available at biosoft.kaist.ac.kr/ecm.-
dc.publisherMary Ann Liebert, Inc-
dc.titlePrediction of extracellular matrix proteins based on distinctive sequence and domain characteristics-
dc.title.alternativePrediction of extracellular matrix proteins based on distinctive sequence and domain characteristics-
dc.typeArticle-
dc.citation.titleJournal of Computational Biology-
dc.citation.number1-
dc.citation.endPage105-
dc.citation.startPage97-
dc.citation.volume17-
dc.contributor.affiliatedAuthorTaewoo Ryu-
dc.contributor.alternativeName정주현-
dc.contributor.alternativeName유태우-
dc.contributor.alternativeName황용득-
dc.contributor.alternativeName이은정-
dc.contributor.alternativeName이도헌-
dc.identifier.bibliographicCitationJournal of Computational Biology, vol. 17, no. 1, pp. 97-105-
dc.identifier.doi10.1089/cmb.2008.0236-
dc.subject.keywordECM-
dc.subject.keywordExtracellular matrix proteins-
dc.subject.keywordProtein localization-
dc.subject.keywordRandom Forest-
dc.subject.keywordSupport vector machine-
dc.subject.localECM-
dc.subject.localExtracellular matrix proteins-
dc.subject.localProtein localization-
dc.subject.localRandom forest-
dc.subject.localrandom forest-
dc.subject.localRandom forests-
dc.subject.localRandom Forest-
dc.subject.localSupport vector machine-
dc.description.journalClassY-
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