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dc.contributor.authorCharoenkwan, Phasit
dc.contributor.authorNantasenamat, Chanin
dc.contributor.authorHasan, Md Mehedi
dc.contributor.authorMoni, Mohammad Ali
dc.contributor.authorLio, Pietro
dc.contributor.authorShoombuatong, Watshara
dc.date.accessioned2021-11-06T00:31:22Z
dc.date.available2021-11-06T00:31:22Z
dc.date.issued2021-08-19
dc.identifier.issn1422-0067
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330375
dc.description.abstractAccurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.
dc.languageeng
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectbioinformatics
dc.subjectbitter peptide
dc.subjectclassification
dc.subjectfeature selection
dc.subjectmachine learning
dc.subjectsupport vector machine
dc.subjectAlgorithms
dc.subjectBenchmarking
dc.subjectHumans
dc.subjectMachine Learning
dc.subjectPeptide Fragments
dc.subjectPredictive Value of Tests
dc.subjectSoftware
dc.subjectSupport Vector Machine
dc.subjectTaste
dc.titleiBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.
dc.typeArticle
prism.issueIdentifier16
prism.publicationNameInternational Journal of Molecular Sciences
prism.volume22
dc.identifier.doi10.17863/CAM.77818
dcterms.dateAccepted2021-08-17
rioxxterms.versionofrecord10.3390/ijms22168958
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-08-17
dc.contributor.orcidLio, Pietro [0000-0002-0540-5053]
dc.identifier.eissn1422-0067
rioxxterms.typeJournal Article/Review
cam.issuedOnline2021-08-19


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International