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dc.contributor.authorKandaswamy, Krishna Kumar Umar
dc.contributor.authorGanesan, Pugalenthi
dc.contributor.authorKalies, Kai Uwe
dc.contributor.authorHartmann, Enno
dc.contributor.authorMartinetz, Thomas M.
dc.date.accessioned2015-08-03T10:43:35Z
dc.date.available2015-08-03T10:43:35Z
dc.date.issued2013-01
dc.identifier.citationKandaswamy, K. K., Pugalenthi, G., Kalies, K.-U., Hartmann, E., & Martinetz, T. (2013). EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection. Journal of Theoretical Biology, 317, 377–383. doi:10.1016/j.jtbi.2012.10.015
dc.identifier.issn00225193
dc.identifier.pmid23123454
dc.identifier.doi10.1016/j.jtbi.2012.10.015
dc.identifier.urihttp://hdl.handle.net/10754/562580
dc.description.abstractThe extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases. In this work, we report a random forest approach, EcmPred, for the prediction of ECM proteins from protein sequences. EcmPred was trained on a dataset containing 300 ECM and 300 non-ECM and tested on a dataset containing 145 ECM and 4187 non-ECM proteins. EcmPred achieved 83% accuracy on the training and 77% on the test dataset. EcmPred predicted 15 out of 20 experimentally verified ECM proteins. By scanning the entire human proteome, we predicted novel ECM proteins validated with gene ontology and InterPro. The dataset and standalone version of the EcmPred software is available at http://www.inb.uni-luebeck.de/tools-demos/Extracellular_matrix_proteins/EcmPred. © 2012 Elsevier Ltd.
dc.description.sponsorshipThis work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/1]. KKK acknowledges Dr. Bianca Habermann, Max Planck Institute for Biology of Ageing, Germany for her support.
dc.publisherElsevier BV
dc.subjectExtracellular proteins
dc.subjectHuman proteome
dc.subjectMaximum relevance minimum redundancy (mRMR)
dc.subjectRandom forest
dc.subjectSequence properties
dc.titleEcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection
dc.typeArticle
dc.contributor.departmentBioscience Core Lab
dc.contributor.departmentCore Labs
dc.identifier.journalJournal of Theoretical Biology
dc.contributor.institutionInstitute for Neuro- and Bioinformatics, University of Luebeck, Germany
dc.contributor.institutionGraduate School for Computing in Medicine and Life Sciences, University of Luebeck, Germany
dc.contributor.institutionMax Planck Institute for Biology of Ageing, Germany
dc.contributor.institutionCentre for Structural and Cell Biology in Medicine, Institute of Biology, University of Luebeck, Germany
kaust.personGanesan, Pugalenthi


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