A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features

Nguyen Quoc Khanh Le, Duyen Thi Do, Trinh Trung Duong Nguyen, Quynh Anh Le

研究成果: 雜誌貢獻文章同行評審

摘要

Krüppel-like factors (KLF) refer to a group of conserved zinc finger-containing transcription factors that are involved in various physiological and biological processes, including cell proliferation, differentiation, development, and apoptosis. Some bioinformatics methods such as sequence similarity searches, multiple sequence alignment, phylogenetic reconstruction, and gene synteny analysis have also been proposed to broaden our knowledge of KLF proteins. In this study, we proposed a novel computational approach by using machine learning on features calculated from primary sequences. To detail, our XGBoost-based model is efficient in identifying KLF proteins, with accuracy of 96.4% and MCC of 0.704. It also holds a promising performance when testing our model on an independent dataset. Therefore, our model could serve as an useful tool to identify new KLF proteins and provide necessary information for biologists and researchers in KLF proteins. Our machine learning source codes as well as datasets are freely available at https://github.com/khanhlee/KLF-XGB.
原文英語
文章編號145643
期刊Gene
787
DOIs
出版狀態已發佈 - 六月 30 2021

ASJC Scopus subject areas

  • 遺傳學

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