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dc.contributor.authorArafat, Md Easin
dc.contributor.authorAhmad, Md Wakil
dc.contributor.authorShovan, SM
dc.contributor.authorDehzangi, Abdollah
dc.contributor.authorDipta, Shubhashis Roy
dc.contributor.authorHasan, Md Al Mehedi
dc.contributor.authorTaherzadeh, Ghazaleh
dc.contributor.authorShatabda, Swakkhar
dc.contributor.authorSharma, Alok
dc.date.accessioned2020-09-10T04:25:29Z
dc.date.available2020-09-10T04:25:29Z
dc.date.issued2020
dc.identifier.issn2073-4425
dc.identifier.doi10.3390/genes11091023
dc.identifier.urihttp://hdl.handle.net/10072/397318
dc.description.abstractPost Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute
dc.relation.ispartofissue9
dc.relation.ispartofjournalGenes
dc.relation.ispartofvolume11
dc.subject.fieldofresearchGenetics
dc.subject.fieldofresearchcode3105
dc.subject.keywordsbi-peptide evolutionary features
dc.subject.keywordsextra-trees classifier
dc.subject.keywordslysine Glutarylation
dc.subject.keywordsmachine learning
dc.subject.keywordspost-translational modification
dc.titleAccurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationArafat, ME; Ahmad, MW; Shovan, SM; Dehzangi, A; Dipta, SR; Hasan, MAM; Taherzadeh, G; Shatabda, S; Sharma, A, Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features., Genes, 2020, 11 (9), pp. 1023-1023
dcterms.dateAccepted2020-08-27
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-09-10T04:16:27Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2020 The Authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution 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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