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DeepAFP:基于深度学习的有效计算框架,用于识别基于深度学习的抗真菌.

Lantian Yao1,2, Yuntian Zhang3, Wenshuo Li2

  • 1Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, China.

Protein science : a publication of the Protein Society
|August 18, 2023
PubMed
概括

研究人员开发了DeepAFP,这是一个深度学习工具,用于识别抗真菌 (AFP). 这种人工智能框架准确地预测了AFP,为传统的抗真菌药物开发提供了一个更快的替代方案,用于对抗真菌感染.

关键词:
抗真菌类的抗真菌.深度学习是一种深度学习.发现药物的发现.序列分析分析的序列分析.

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 真菌感染对全球健康构成重大挑战.
  • 抗真菌 (AFPs) 是传统抗真菌药物的一个有希望的替代品,因为它们的毒性和耐药性很低.
  • 开发有效和高效的方法来识别AFP至关重要.

研究的目的:

  • 开发一个基于深度学习的框架,DeepAFP,以有效和准确地识别抗真菌 (AFP).
  • 为了利用各种特征,包括组成,进化信息和物理化学性质.
  • 整合转移学习以增强体表示和模型性能.

主要方法:

  • 开发一个深度学习框架 (DeepAFP),利用卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM) 层.
  • 结合核的集成以处理多个数据分支:化合物组成,进化信息和物理化学性质.
  • 实施转移学习策略,以改善体表示和模型准确性.

主要成果:

  • 在精选数据集上,DeepAFP实现了高预测性能,准确率为93.29%,在DeepAFP-Main数据集上,F1得分为93.45%.
  • 与现有的抗真菌预测工具相比,该框架表现出优异的性能,建立了最先进的结果.
  • 开发了一个可下载的预测工具,以促进大规模的AFP识别和研究.

结论:

  • DeepAFP提供了一种准确而快速的方法,以最小的资源要求识别抗真菌 (AFP).
  • 开发的工具可以加速新型AFP的发现和开发,有助于治疗真菌感染.
  • DeepAFP框架为除了抗真菌的识别之外的生物序列分析提供了一种有价值的方法.