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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.<\/jats:p>","DOI":"10.1093\/bib\/bbab172","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T12:21:10Z","timestamp":1618316470000},"source":"Crossref","is-referenced-by-count":127,"title":["StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides"],"prefix":"10.1093","volume":"22","author":[{"given":"Phasit","family":"Charoenkwan","sequence":"first","affiliation":[{"name":"Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wararat","family":"Chiangjong","sequence":"additional","affiliation":[{"name":"Pediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chanin","family":"Nantasenamat","sequence":"additional","affiliation":[{"name":"Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Mehedi","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balachandran","family":"Manavalan","sequence":"additional","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Watshara","family":"Shoombuatong","sequence":"additional","affiliation":[{"name":"Center of Data 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