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.
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