Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/75511
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dc.contributor.authorPhasit Charoenkwanen_US
dc.contributor.authorWararat Chiangjongen_US
dc.contributor.authorChanin Nantasenamaten_US
dc.contributor.authorMd Mehedi Hasanen_US
dc.contributor.authorBalachandran Manavalanen_US
dc.contributor.authorWatshara Shoombuatongen_US
dc.date.accessioned2022-10-16T07:00:23Z-
dc.date.available2022-10-16T07:00:23Z-
dc.date.issued2021-11-05en_US
dc.identifier.issn14774054en_US
dc.identifier.other2-s2.0-85112187431en_US
dc.identifier.other10.1093/bib/bbab172en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85112187431&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/75511-
dc.description.abstractThe release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are derived from pathogens and can be used as diagnostic biomarkers for predicting various stages of disease severity as well as being used as IL-6 inhibitors for the suppression of aggressive multi-signaling immune responses. Thus, the accurate identification of IL-6 inducing peptides is of great importance for investigating their mechanism of action as well as for developing diagnostic and immunotherapeutic applications. This study proposes a novel stacking ensemble model (termed StackIL6) for accurately identifying IL-6 inducing peptides. More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest). To enhance the utility of baseline models, they were effectively and systematically integrated through a stacking strategy to build the final meta-based model. Extensive benchmarking experiments demonstrated that StackIL6 could achieve significantly better performance than the existing method (IL6PRED) and outperformed its constituent baseline models on both training and independent test datasets, which thereby support its excellent discrimination and generalization abilities. To facilitate easy access to the StackIL6 model, it was established as a freely available web server accessible at http://camt.pythonanywhere.com/StackIL6. It is anticipated that StackIL6 can help to facilitate rapid screening of promising IL-6 inducing peptides for the development of diagnostic and immunotherapeutic applications in the future.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectComputer Scienceen_US
dc.titleStackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptidesen_US
dc.typeJournalen_US
article.title.sourcetitleBriefings in bioinformaticsen_US
article.volume22en_US
article.stream.affiliationsRamathibodi Hospitalen_US
article.stream.affiliationsKyushu Institute of Technologyen_US
article.stream.affiliationsAjou University School of Medicineen_US
article.stream.affiliationsMahidol Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
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