Bitter-RF: A random forest machine model for recognizing bitter peptides
Yufei Zhang, Yu-Fei Zhang, Yuhao Wang, Zhi-Feng Gu, Xianrun Pan, Jian Li, Hui Ding, Yang Zhang, Yang Zhang, Kejun Deng
Abstract
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.