therapeutic peptide
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2021 ◽  
Vol 17 ◽  
Author(s):  
Ke Yan ◽  
Hongwu Lv ◽  
Yichen Guo ◽  
Jie Wen ◽  
Bin Liu

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. Method: 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.


ChemPhysMater ◽  
2021 ◽  
Author(s):  
Rita Cimino ◽  
Marco Savioli ◽  
Noemi Ferrante Carrante ◽  
Ernesto Placidi ◽  
Hilda Garay-Perez ◽  
...  

Drugs in R&D ◽  
2021 ◽  
Author(s):  
Sajjan Rajpoot ◽  
Tomokazu Ohishi ◽  
Ashutosh Kumar ◽  
Qiuwei Pan ◽  
Sreeparna Banerjee ◽  
...  

2021 ◽  
Author(s):  
Tianqi Nie ◽  
Wei Wang ◽  
Xiaohu Liu ◽  
Yanan Wang ◽  
Keyang Li ◽  
...  

Author(s):  
Abbas Alibakhshi ◽  
Shahrzad Ahangarzadeh ◽  
Leila Beikmohammadi ◽  
Behnoush Soltanmohammadi ◽  
Armina Alagheband Bahrami ◽  
...  

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