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dc.contributor.authorUddin, Md Raihan
dc.contributor.authorSharma, Alok
dc.contributor.authorFarid, Dewan Md
dc.contributor.authorRahman, Md Mahmudur
dc.contributor.authorDehzangi, Abdollah
dc.contributor.authorShatabda, Swakkhar
dc.date.accessioned2019-06-26T12:30:44Z
dc.date.available2019-06-26T12:30:44Z
dc.date.issued2018
dc.identifier.issn0022-5193
dc.identifier.doi10.1016/j.jtbi.2018.02.002
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherElsevier
dc.publisher.placeUnited Kingdom
dc.relation.ispartofpagefrom138
dc.relation.ispartofpageto146
dc.relation.ispartofjournalJournal of Theoretical Biology
dc.relation.ispartofvolume443
dc.subject.fieldofresearchBiological Sciences not elsewhere classified
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode069999
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.titleEvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionPost-print
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.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok
gro.griffith.authorShatabda, Swakkhar


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