دورية أكاديمية
iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features
| العنوان: | iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features |
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| المؤلفون: | Honghao Li, Liangzhen Jiang, Kaixiang Yang, Shulin Shang, Mingxin Li, Zhibin Lv |
| المصدر: | International Journal of Molecular Sciences, Vol 25, Iss 13, p 7049 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Biology (General) LCC:Chemistry |
| مصطلحات موضوعية: | neuropeptide identification, machine learning, protein language model, Biology (General), QH301-705.5, Chemistry, QD1-999 |
| الوصف: | Neuropeptides are biomolecules with crucial physiological functions. Accurate identification of neuropeptides is essential for understanding nervous system regulatory mechanisms. However, traditional analysis methods are expensive and laborious, and the development of effective machine learning models continues to be a subject of current research. Hence, in this research, we constructed an SVM-based machine learning neuropeptide predictor, iNP_ESM, by integrating protein language models Evolutionary Scale Modeling (ESM) and Unified Representation (UniRep) for the first time. Our model utilized feature fusion and feature selection strategies to improve prediction accuracy during optimization. In addition, we validated the effectiveness of the optimization strategy with UMAP (Uniform Manifold Approximation and Projection) visualization. iNP_ESM outperforms existing models on a variety of machine learning evaluation metrics, with an accuracy of up to 0.937 in cross-validation and 0.928 in independent testing, demonstrating optimal neuropeptide recognition capabilities. We anticipate improved neuropeptide data in the future, and we believe that the iNP_ESM model will have broader applications in the research and clinical treatment of neurological diseases. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1422-0067 1661-6596 |
| Relation: | https://www.mdpi.com/1422-0067/25/13/7049; https://doaj.org/toc/1661-6596; https://doaj.org/toc/1422-0067 |
| DOI: | 10.3390/ijms25137049 |
| URL الوصول: | https://doaj.org/article/05965dcdda014f7187646803a42c6534 |
| رقم الأكسشن: | edsdoj.05965dcdda014f7187646803a42c6534 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14220067 16616596 |
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| DOI: | 10.3390/ijms25137049 |