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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

TP-MV: Therapeutic Peptides Prediction by Multi-view Learning

Author(s): Ke Yan, Hongwu Lv, Jie Wen, Yichen Guo and Bin Liu*

Volume 17, Issue 2, 2022

Published on: 29 December, 2021

Page: [174 - 183] Pages: 10

DOI: 10.2174/1574893617666211220153429

Price: $65

Abstract

Background: Therapeutic peptide prediction is critical for drug development and therapy. Researchers have been studying this essential task, developing several computational methods to identify different therapeutic peptide types.

Objective: Most predictors are the specific methods for certain peptides. Currently, developing methods to predict the presence of multiple peptides remains a challenging problem. Moreover, it is still challenging to combine different features to make the therapeutic prediction.

Methods: In this paper, we proposed a new ensemble method TP-MV for general therapeutic peptide recognition. TP-MV is developed using the stacking framework in conjunction with the KNN, SVM, ET, RF, and XGB. Then TP-MV constructs a multi-view learning model as meta-classifiers to extract the discriminative feature for different peptides.

Results: In the experiment, the proposed method outperforms the other existing methods on the benchmark datasets, indicating that the proposed method has the ability to predict multiple therapeutic peptides simultaneously.

Conclusion: The TP-MV is a useful tool for predicting therapeutic peptides.

Keywords: Therapeutic peptide recognition, stacking method, multi-view learning method, ensemble learning, sequence analysis, AAC.

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