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BERT4Bitter: A bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides

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dc.contributor.authorCharoenkwan, P-
dc.contributor.authorNantasenamat, C-
dc.contributor.authorHasan, MM-
dc.contributor.authorManavalan, B-
dc.contributor.authorShoombuatong, W-
dc.date.accessioned2023-01-26T06:10:13Z-
dc.date.available2023-01-26T06:10:13Z-
dc.date.issued2021-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/24034-
dc.description.abstractMotivation: The identification of bitter peptides through experimental approaches is an expensive and timeconsuming endeavor. Due to the huge number of newly available peptide sequences in the post-genomic era, the development of automated computational models for the identification of novel bitter peptides is highly desirable. Results: In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)- based model for predicting bitter peptides directly from their amino acid sequence without using any structural information. To the best of our knowledge, this is the first time a BERT-based model has been employed to identify bitter peptides. Compared to widely used machine learning models, BERT4Bitter achieved the best performance with an accuracy of 0.861 and 0.922 for cross-validation and independent tests, respectively. Furthermore, extensive empirical benchmarking experiments on the independent dataset demonstrated that BERT4Bitter clearly outperformed the existing method with improvements of 8.0% accuracy and 16.0% Matthews coefficient correlation, highlighting the effectiveness and robustness of BERT4Bitter. We believe that the BERT4Bitter method proposed herein will be a useful tool for rapidly screening and identifying novel bitter peptides for drug development and nutritional research.-
dc.language.isoen-
dc.titleBERT4Bitter: A bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides-
dc.typeArticle-
dc.identifier.pmid33638635-
dc.contributor.affiliatedAuthorManavalan, B-
dc.type.localJournal Papers-
dc.identifier.doi10.1093/bioinformatics/btab133-
dc.citation.titleBioinformatics (Oxford, England)-
dc.citation.volume37-
dc.citation.number17-
dc.citation.date2021-
dc.citation.startPage2556-
dc.citation.endPage2562-
dc.identifier.bibliographicCitationBioinformatics (Oxford, England), 37(17). : 2556-2562, 2021-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.identifier.eissn1367-4811-
dc.relation.journalidJ013674803-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Physiology
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