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dc.contributor.authorAhmed, S
dc.contributor.authorHossain, Z
dc.contributor.authorUddin, M
dc.contributor.authorTaherzadeh, G
dc.contributor.authorSharma, A
dc.contributor.authorShatabda, S
dc.contributor.authorDehzangi, A
dc.date.accessioned2020-12-22T00:03:10Z
dc.date.available2020-12-22T00:03:10Z
dc.date.issued2020
dc.identifier.issn2001-0370
dc.identifier.doi10.1016/j.csbj.2020.10.032
dc.identifier.urihttp://hdl.handle.net/10072/400475
dc.description.abstractRNA modification is an essential step towards generation of new RNA structures. Such modification is potentially able to modify RNA function or its stability. Among different modifications, 5-Hydroxymethylcytosine (5hmC) modification of RNA exhibit significant potential for a series of biological processes. Understanding the distribution of 5hmC in RNA is essential to determine its biological functionality. Although conventional sequencing techniques allow broad identification of 5hmC, they are both time-consuming and resource-intensive. In this study, we propose a new computational tool called iRNA5hmC-PS to tackle this problem. To build iRNA5hmC-PS we extract a set of novel sequence-based features called Position-Specific Gapped k-mer (PSG k-mer) to obtain maximum sequential information. Our feature analysis shows that our proposed PSG k-mer features contain vital information for the identification of 5hmC sites. We also use a group-wise feature importance calculation strategy to select a small subset of features containing maximum discriminative information. Our experimental results demonstrate that iRNA5hmC-PS is able to enhance the prediction performance, dramatically. iRNA5hmC-PS achieves 78.3% prediction performance, which is 12.8% better than those reported in the previous studies. iRNA5hmC-PS is publicly available as an online tool at http://103.109.52.8:81/iRNA5hmC-PS. Its benchmark dataset, source codes, and documentation are available at https://github.com/zahid6454/iRNA5hmC-PS.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom3528
dc.relation.ispartofjournalComputational and Structural Biotechnology Journal
dc.relation.ispartofvolume18
dc.subject.fieldofresearchNumerical and computational mathematics
dc.subject.fieldofresearchcode4903
dc.subject.keywordsLogistic regression
dc.subject.keywordsPosition-specific gapped k-mer
dc.subject.keywordsPosition-specific k-mer
dc.subject.keywordsRNA 5-hydroxymethylcytosine modification
dc.subject.keywordsSequence-based feature
dc.titleAccurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationAhmed, S; Hossain, Z; Uddin, M; Taherzadeh, G; Sharma, A; Shatabda, S; Dehzangi, A, Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors, Computational and Structural Biotechnology Journal, 2020, 18, pp. 3528
dcterms.dateAccepted2020-10-30
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-12-21T23:58:21Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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