Implicit motif distribution based hybrid computational kernel for sequence classification

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dc.citation.epage1436en_US
dc.citation.issueNumber8en_US
dc.citation.spage1429en_US
dc.citation.volumeNumber21en_US
dc.contributor.authorAtalay, V.en_US
dc.contributor.authorCetin Atalay, R.en_US
dc.date.accessioned2016-02-08T10:23:44Z
dc.date.available2016-02-08T10:23:44Z
dc.date.issued2005en_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.description.abstractMotivation: We designed a general computational kernel for classification problems that require specific motif extraction and search from sequences. Instead of searching for explicit motifs, our approach finds the distribution of implicit motifs and uses as a feature for classification. Implicit motif distribution approach may be used as modus operandi for bioinformatics problems that require specific motif extraction and search, which is otherwise computationally prohibitive. Results: A system named P2SL that infer protein subcellular targeting was developed through this computational kernel. Targeting-signal was modeled by the distribution of subsequence occurrences (implicit motifs) using self-organizing maps. The boundaries among the classes were then determined with a set of support vector machines. P2SL hybrid computational system achieved ∼81% of prediction accuracy rate over ER targeted, cytosolic, mitochondrial and nuclear protein localization classes. P2SL additionally offers the distribution potential of proteins among localization classes, which is particularly important for proteins, shuttle between nucleus and cytosol. © The Author 2004. Published by Oxford University Press. All rights reserved.en_US
dc.identifier.doi10.1093/bioinformatics/bti212en_US
dc.identifier.issn1367-4803
dc.identifier.urihttp://hdl.handle.net/11693/24076
dc.language.isoEnglishen_US
dc.publisherOxford University Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1093/bioinformatics/bti212en_US
dc.source.titleBioinformaticsen_US
dc.subjectBeta cateninen_US
dc.subjectBRCA1 proteinen_US
dc.subjectHexokinaseen_US
dc.subjectImmunoglobulin enhancer binding proteinen_US
dc.subjectProtein bcl 2en_US
dc.subjectProtein kinase C alphaen_US
dc.subjectProtein p53en_US
dc.subjectTranscription factor E2F1en_US
dc.subjectAmino acid sequenceen_US
dc.subjectAmino Acid Motifsen_US
dc.titleImplicit motif distribution based hybrid computational kernel for sequence classificationen_US
dc.typeArticleen_US

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