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dc.contributor.authorChaudhari, Meenal
dc.contributor.authorThapa, Niraj
dc.contributor.authorRoy, Kaushik
dc.contributor.authorNewman, Robert H.
dc.contributor.authorHiroto, Saigo
dc.contributor.authorKC, Dukka B.
dc.date.accessioned2020-07-06T14:16:59Z
dc.date.available2020-07-06T14:16:59Z
dc.date.issued2020-06-03
dc.identifier.citationM. Chaudhari, N. Thapa, K. Roy, R. H. Newman, H. Saigo and D. B. K. C., DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins. Mol. Omics, 2020 Advanced article.en_US
dc.identifier.issn2515-4184
dc.identifier.urihttps://doi.org/10.1039/d0mo00025f
dc.identifier.urihttps://soar.wichita.edu/handle/10057/18580
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractMethylation, which is one of the most prominent post-translational modifications on proteins, regulates many important cellular functions. Though several model-based methylation site predictors have been reported, all existing methods employ machine learning strategies, such as support vector machines and random forest, to predict sites of methylation based on a set of "hand-selected" features. As a consequence, the subsequent models may be biased toward one set of features. Moreover, due to the large number of features, model development can often be computationally expensive. In this paper, we propose an alternative approach based on deep learning to predict arginine methylation sites. Our model, which we termed DeepRMethylSite, is computationally less expensive than traditional feature-based methods while eliminating potential biases that can arise through features selection. Based on independent testing on our dataset, DeepRMethylSite achieved efficiency scores of 68%, 82% and 0.51 with respect to sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC), respectively. Importantly, in side-by-side comparisons with other state-of-the-art methylation site predictors, our method performs on par or better in all scoring metrics tested.en_US
dc.language.isoen_USen_US
dc.publisherRoyal Society of Chemistryen_US
dc.relation.ispartofseriesMolecular Omics;2020
dc.titleDeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteinsen_US
dc.typeArticleen_US
dc.rights.holder© Royal Society of Chemistry 2020en_US


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