Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences.
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What This Study Found
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Trust & Context
- Original Title:
- Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences.
- Published In:
- Scientific reports, 14(1), 14387 (2024)
- Authors:
- Lee, Yun-Chen, Yu, Jen-Chieh, Ni, Kuan, Lin, Yu-Chuan, Chen, Ching-Tai
- Database ID:
- RPEP-08652
Evidence Hierarchy
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Cite This Study
https://rethinkpeptides.com/research/RPEP-08652APA
Lee, Yun-Chen; Yu, Jen-Chieh; Ni, Kuan; Lin, Yu-Chuan; Chen, Ching-Tai. (2024). Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences.. Scientific reports, 14(1), 14387. https://doi.org/10.1038/s41598-024-65062-9
MLA
Lee, Yun-Chen, et al. "Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences.." Scientific reports, 2024. https://doi.org/10.1038/s41598-024-65062-9
RethinkPeptides
RethinkPeptides Research Database. "Improved prediction of anti-angiogenic peptides based on mac..." RPEP-08652. Retrieved from https://rethinkpeptides.com/research/lee-2024-improved-prediction-of-antiangiogenic
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Study data sourced from PubMed, a service of the U.S. National Library of Medicine, National Institutes of Health.
This study breakdown was produced by the RethinkPeptides research team. We analyze and report published research findings without making health recommendations. All interpretations are based solely on the published abstract and study data.