Therapeutic peptides are important for understanding the correlation between peptides and their therapeutic diagnostic potential. The therapeutic peptides can be further divided into different types based on therapeutic function sharing different characteristics. Although some computational approaches have been proposed to predict different types of therapeutic peptides, they failed to accurately predict all types of therapeutic peptides. In this study, a predictor called PreTP-EL has been proposed via employing the ensemble learning approach to fuse the different features and machine learning techniques in order to capture the different characteristics of various therapeutic peptides. Experimental results showed that PreTP-EL outperformed other competing methods. Availability and implementation: A user-friendly web-server of PreTP-EL predictor is available at http://bliulab.net/PreTP-EL.

中文翻译：

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PreTP-EL：基于集成学习的治疗性肽预测

治疗性肽对于了解肽与其治疗诊断潜力之间的相关性很重要。治疗性肽可根据具有不同特征的治疗功能进一步分为不同类型。尽管已经提出了一些计算方法来预测不同类型的治疗性肽，但它们未能准确预测所有类型的治疗性肽。在这项研究中，通过采用集成学习方法融合不同的特征和机器学习技术，提出了一种称为 PreTP-EL 的预测器，以捕捉各种治疗性肽的不同特征。实验结果表明 PreTP-EL 优于其他竞争方法。可用性和实施​​：