Bitter-RF: A random forest machine model for recognizing bitter peptides.

Autor:	Zhang YF; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China., Wang YH; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China., Gu ZF; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China., Pan XR; Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China., Li J; School of Basic Medical Sciences, Chengdu University, Chengdu, China., Ding H; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China., Zhang Y; Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China., Deng KJ; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
Jazyk:	angličtina
Zdroj:	Frontiers in medicine [Front Med (Lausanne)] 2023 Jan 26; Vol. 10, pp. 1052923. Date of Electronic Publication: 2023 Jan 26 (Print Publication: 2023).
DOI:	10.3389/fmed.2023.1052923
Abstrakt:	Introduction: Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides. Methods: In this study, we developed a Random forest (RF)-based model, called Bitter-RF, using sequence information of the bitter peptide. Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set. Results: The proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides. Discussion: We hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Zhang, Wang, Gu, Pan, Li, Ding, Zhang and Deng.)
Databáze:	MEDLINE
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