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dc.contributor.authorPhasit Charoenkwanen_US
dc.contributor.authorChanin Nantasenamaten_US
dc.contributor.authorMd Mehedi Hasanen_US
dc.contributor.authorWatshara Shoombuatongen_US
dc.date.accessioned2020-10-14T08:25:44Z-
dc.date.available2020-10-14T08:25:44Z-
dc.date.issued2020-06-15en_US
dc.identifier.issn10960309en_US
dc.identifier.issn00032697en_US
dc.identifier.other2-s2.0-85083874426en_US
dc.identifier.other10.1016/j.ab.2020.113747en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85083874426&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/70216-
dc.description.abstract© 2020 Elsevier Inc. In spite of the repertoire of existing cancer therapies, the ongoing recurrence and new cases of cancer poses a challenging health concern that prompts for novel and effective treatment. Cancer immunotherapy represents a promising venue for treatment by harnessing the body's immune system to combat cancer. Therefore, the identification of tumor T cell antigen represents an exciting area to explore. Computational tools have been instrumental in the identification of tumor T cell antigens and it is highly desirable to attain highly accurate models in a timely fashion from large volumes of peptides generated in the post-genomic era. In this study, we present a reliable, accurate, unbiased and automated sequence-based predictor named iTTCA-Hybrid for identifying tumor T cell antigens. The iTTCA-Hybrid approach proposed herein employs two robust machine learning models (e.g. support vector machine and random forest) constructed using five feature encoding strategies (i.e. amino acid composition, dipeptide composition, pseudo amino acid composition, distribution of amino acid properties in sequences and physicochemical properties derived from the AAindex). Rigorous independent test indicated that the iTTCA-Hybrid approach achieved an accuracy and area under the curve of 73.60% and 0.783, respectively, which corresponds to 4% and 7% performance increase than those of existing methods thereby indicating the superiority of the proposed model. To the best of our knowledge, the iTTCA-Hybrid is the first free web server (Available at http://camt.pythonanywhere.com/iTTCA-Hybrid) for identifying tumor T cell antigens presented by the MHC class I. The proposed web server allows robust predictions to be made without the need to develop in-house prediction models.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.titleiTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representationen_US
dc.typeJournalen_US
article.title.sourcetitleAnalytical Biochemistryen_US
article.volume599en_US
article.stream.affiliationsKyushu Institute of Technologyen_US
article.stream.affiliationsMahidol Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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