用生成对抗网络和集成学习提升抗菌肽识别准确率
Improvement of AMPs Identification with Generative Adversarial Network and Ensemble Classification
- 融合多编码方法与深度神经网络处理数据不平衡问题
- 模型在多个数据集上准确率优于现有方法
- 适合医药研发人员用于抗菌肽筛选
抗菌肽的识别在当前时代具有重要意义，是抗生素替代品的重要候选，广泛应用于药物设计及抵御微生物感染。人工智能算法显著提升了此类肽的识别效率。本研究通过融合不同视角的最佳编码方式，并结合深度神经网络解决数据不平衡问题，优化了抗菌肽预测方法。实验结果表明，该方法在预测准确率和效率方面均有显著提升，性能优于现有方法。该技术在医学与制药领域具有高应用价值。
原文摘要 · Abstract (English)
Identification of antimicrobial peptides is an important and necessary issue in today's era. Antimicrobial peptides are essential as an alternative to antibiotics for biomedical applications and many other practical applications. These oligopeptides are useful in drug design and cause innate immunity against microorganisms. Artificial intelligence algorithms have played a significant role in the ease of identifying these peptides.This research is improved by improving proposed method in the field of antimicrobial peptides prediction. Suggested method is improved by combining the best coding method from different perspectives, In the following a deep neural network to balance the imbalanced combined datasets. The results of this research show that the proposed method have a significant improvement in the accuracy and efficiency of the prediction of antimicrobial peptides and are able to provide the best results compared to the existing methods. These development in the field of prediction and classification of antimicrobial peptides, basically in the fields of medicine and pharmaceutical industries, have high effectiveness and application.
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