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dc.contributor.authorSaini, Harsh
dc.contributor.authorRaicar, Gaurav
dc.contributor.authorDehzangi, Abdollah
dc.contributor.authorLal, Sunil
dc.contributor.authorSharma, Alok
dc.date.accessioned2017-06-12T04:26:28Z
dc.date.available2017-06-12T04:26:28Z
dc.date.issued2015
dc.identifier.issn0022-5193
dc.identifier.doi10.1016/j.jtbi.2015.08.020
dc.identifier.urihttp://hdl.handle.net/10072/101470
dc.description.abstractProtein subcellular localization is an important topic in proteomics since it is related to a protein׳s overall function, helps in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to a particular subcellular location. This technique builds a statistical model based on maximum likelihood. It is able to deal effectively with high dimensionality that hinders other traditional classifiers such as Support Vector Machines or k-Nearest Neighbours without sacrificing performance. This approach has been evaluated by predicting subcellular localizations of Gram positive and Gram negative bacterial proteins.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom25
dc.relation.ispartofpageto33
dc.relation.ispartofissue33
dc.relation.ispartofjournalJournal of Theoretical Biology
dc.relation.ispartofvolume386
dc.subject.fieldofresearchMicrobiology not elsewhere classified
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode060599
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.titleSubcellular localization for Gram positive and Gram negative bacterial proteins using linear interpolation smoothing model
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2015 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (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.authorDehzangi, Iman


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