AUTHOR=Meng Chaolu , Hu Yang , Zhang Ying , Guo Fei 

TITLE=PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 8 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2020.00245

DOI=10.3389/fbioe.2020.00245

ISSN=2296-4185

ABSTRACT=Polystyrene binding peptides play a key role in the immobilization process. The correct identification of polystyrene binding peptides is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a 5-fold cross validation test, this model achieves 90.38%, 84.62%, 87.50% and 0.90 SN, SP, ACC and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible athttp://server.malab.cn/PSBP-SVM/index.jsp.