دورية أكاديمية
NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information
| العنوان: | NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information |
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| المؤلفون: | Li-Na Jia, Xin Yan, Zhu-Hong You, Xi Zhou, Li-Ping Li, Lei Wang, Ke-Jian Song |
| المصدر: | Evolutionary Bioinformatics, Vol 16 (2020) |
| بيانات النشر: | SAGE Publishing, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Evolution |
| مصطلحات موضوعية: | Evolution, QH359-425 |
| الوصف: | The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing. Therefore, fast and accurate computational methods are needed to effectively predict SIPs. In this study, we designed a new method, NLPEI, for predicting SIPs based on natural language understanding theory and evolutionary information. Specifically, we first understand the protein sequence as natural language and use natural language processing algorithms to extract its features. Then, we use the Position-Specific Scoring Matrix (PSSM) to represent the evolutionary information of the protein and extract its features through the Stacked Auto-Encoder (SAE) algorithm of deep learning. Finally, we fuse the natural language features of proteins with evolutionary features and make accurate predictions by Extreme Learning Machine (ELM) classifier. In the SIPs gold standard data sets of human and yeast, NLPEI achieved 94.19% and 91.29% prediction accuracy. Compared with different classifier models, different feature models, and other existing methods, NLPEI obtained the best results. These experimental results indicated that NLPEI is an effective tool for predicting SIPs and can provide reliable candidates for biological experiments. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1176-9343 11769343 |
| Relation: | https://doaj.org/toc/1176-9343 |
| DOI: | 10.1177/1176934320984171 |
| URL الوصول: | https://doaj.org/article/38a773bfeaf64606bb94b01d8f2af248 |
| رقم الأكسشن: | edsdoj.38a773bfeaf64606bb94b01d8f2af248 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 11769343 |
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| DOI: | 10.1177/1176934320984171 |