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dc.contributor.authorKandaswamy, Krishna Kumar
dc.contributor.authorGanesan, Pugalenthi
dc.contributor.authorHazrati, Mehrnaz Khodam
dc.contributor.authorKalies, Kai-Uwe
dc.contributor.authorMartinetz, Thomas
dc.date.accessioned2014-08-27T09:52:37Z
dc.date.available2014-08-27T09:52:37Z
dc.date.issued2011-08-17
dc.identifier.citationKandaswamy K, Pugalenthi G, Hazrati M, Kalies K-U, Martinetz T (2011) BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection. BMC Bioinformatics 12: 345. doi:10.1186/1471-2105-12-345.
dc.identifier.issn14712105
dc.identifier.pmid21849049
dc.identifier.doi10.1186/1471-2105-12-345
dc.identifier.urihttp://hdl.handle.net/10754/325467
dc.description.abstractBackground: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.
dc.language.isoen
dc.publisherSpringer Nature
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.subjectBiotechnological applications
dc.subjectHypothetical protein
dc.subjectPhysicochemical property
dc.subjectPrediction accuracy
dc.subjectProminent features
dc.subjectSequence informations
dc.subjectSequence similarity
dc.subjectTechnological advances
dc.subjectBiology
dc.subjectForecasting
dc.subjectPhosphorescence
dc.subjectProteins
dc.subjectSupport vector machines
dc.subjectBioluminescence
dc.subjectFungi
dc.subjectHexapoda
dc.subjectphotoprotein
dc.subjectchemistry
dc.subjectcomputer program
dc.subjectprobability
dc.subjectsupport vector machine
dc.subjectLuminescent Proteins
dc.subjectMarkov Chains
dc.subjectSoftware
dc.subjectSupport Vector Machines
dc.titleBLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection
dc.typeArticle
dc.contributor.departmentBioscience Core Lab
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalBMC Bioinformatics
dc.identifier.pmcidPMC3176267
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionInstitute for Neuro- and Bioinformatics, University of Lbeck, 23538 Lbeck, Germany
dc.contributor.institutionGraduate School for Computing in Medicine and Life Sciences, University of Lbeck, 23538 Lbeck, Germany
dc.contributor.institutionInstitute for Signal Processing, University of Lbeck, 23538 Lbeck, Germany
dc.contributor.institutionCentre for Structural and Cell Biology in Medicine, Institute of Biology, University of Lbeck, Germany
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personGanesan, Pugalenthi
refterms.dateFOA2018-06-14T04:34:30Z
dc.date.published-online2011-08-17
dc.date.published-print2011


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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.