ArticleFrontiers in bioengineering and biotechnology2020
PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- AI-driven rational design of promiscuous and selective plastic-binding peptides.Chemical science · 2025Article
- CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.Briefings in bioinformatics · 2024Article
- In Silico Design and Analysis of Plastic-Binding Peptides.The journal of physical chemistry. B · 2023Article
- Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.Computational and structural biotechnology journal · 2023Review
- A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia.Molecular therapy. Nucleic acids · 2022Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 http://server.malab.cn/PSBP-SVM/index.jsp.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.