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dc.contributor.authorUddin, Mden_US
dc.contributor.authorSharma, Aloken_US
dc.contributor.authorFarid, Dewanen_US
dc.contributor.authorRahman, Mden_US
dc.contributor.authorDehzangi, Abdollahen_US
dc.contributor.authorShatabda, Swakkharen_US
dc.date.accessioned2019-05-29T12:37:02Z
dc.date.available2019-05-29T12:37:02Z
dc.date.issued2018en_US
dc.identifier.issn0022-5193en_US
dc.identifier.doi10.1016/j.jtbi.2018.02.002en_US
dc.identifier.urihttp://hdl.handle.net/10072/380647
dc.description.abstractDetermining subcellular localization of proteins is considered as an important step towards understanding their functions. Previous studies have mainly focused solely on Gene Ontology (GO) as the main feature to tackle this problem. However, it was shown that features extracted based on GO is hard to be used for new proteins with unknown GO. At the same time, evolutionary information extracted from Position Specific Scoring Matrix (PSSM) have been shown as another effective features to tackle this problem. Despite tremendous advancement using these sources for feature extraction, this problem still remains unsolved. In this study we propose EvoStruct-Sub which employs predicted structural information in conjunction with evolutionary information extracted directly from the protein sequence to tackle this problem. To do this we use several different feature extraction method that have been shown promising in subcellular localization as well as similar studies to extract effective local and global discriminatory information. We then use Support Vector Machine (SVM) as our classification technique to build EvoStruct-Sub. As a result, we are able to enhance Gram-positive subcellular localization prediction accuracies by up to 5.6% better than previous studies including the studies that used GO for feature extraction.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherElsevieren_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofpagefrom138en_US
dc.relation.ispartofpageto146en_US
dc.relation.ispartofjournalJournal of Theoretical Biologyen_US
dc.relation.ispartofvolume443en_US
dc.subject.fieldofresearchBiological Sciences not elsewhere classifieden_US
dc.subject.fieldofresearchMathematical Sciencesen_US
dc.subject.fieldofresearchBiological Sciencesen_US
dc.subject.fieldofresearchcode069999en_US
dc.subject.fieldofresearchcode01en_US
dc.subject.fieldofresearchcode06en_US
dc.titleEvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural featuresen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dc.type.codeC - Journal Articlesen_US
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.description.versionPost-printen_US
gro.rights.copyright© 2018 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.en_US
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