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dc.contributor.authorShatabda, Swakkhar
dc.contributor.authorSaha, Sanjay
dc.contributor.authorSharma, Alok
dc.contributor.authorDehzangi, Abdollah
dc.date.accessioned2017-12-11T04:25:55Z
dc.date.available2017-12-11T04:25:55Z
dc.date.issued2017
dc.identifier.issn0022-5193
dc.identifier.doi10.1016/j.jtbi.2017.09.022
dc.identifier.urihttp://hdl.handle.net/10072/355090
dc.description.abstractBacteriophage proteins are viruses that can significantly impact on the functioning of bacteria and can be used in phage based therapy. The functioning of Bacteriophage in the host bacteria depends on its location in those host cells. It is very important to know the subcellular location of the phage proteins in a host cell in order to understand their working mechanism. In this paper, we propose iPHLoc-ES, a prediction method for subcellular localization of bacteriophage proteins. We aim to solve two problems: discriminating between host located and non-host located phage proteins and discriminating between the locations of host located protein in a host cell (membrane or cytoplasm). To do this, we extract sets of evolutionary and structural features of phage protein and employ Support Vector Machine (SVM) as our classifier. We also use recursive feature elimination (RFE) to reduce the number of features for effective prediction. On standard dataset using standard evaluation criteria, our method significantly outperforms the state-of-the-art predictor. iPHLoc-ES is readily available to use as a standalone tool from: https://github.com/swakkhar/iPHLoc-ES/ and as a web application from: http://brl.uiu.ac.bd/iPHLoc-ES/.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom229
dc.relation.ispartofpageto237
dc.relation.ispartofjournalJournal of Theoretical Biology
dc.relation.ispartofvolume435
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode089999
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.titleiPHLoc-ES: Identification of bacteriophage protein locations 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.versionAccepted Manuscript (AM)
gro.rights.copyright© 2017 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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