期刊
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
卷 8, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fbioe.2020.00245
关键词
polystyrene binding peptides; support vector machine; bioinformatic; machine learning; identifier
资金
- National Key R&D Program of China [2018YFC0910405]
- National Natural Science Foundation of China [61772362, 61801147]
Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs 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 five-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 at .
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