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pNPs-CapsNet:使用蛋白质语言模型和基于FastText编码的权重多视图特征集成与深度囊神经网络预测神经.

Shahid Akbar1,2, Ali Raza3, Hamid Hussain Awan4

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.

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一个新的计算模型,pNPs-CapsNet,准确地预测神经 (NP) 和非NP. 这种深层囊神经网络方法为识别治疗性NP提供了具有成本效益的替代方案,改善了药物发现.

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科学领域:

  • 生物化学和生物信息学
  • 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:

  • 神经 (NP) 是具有治疗潜力的关键信号分子.
  • 实验性的NP识别是资源密集的.
  • 计算方法为NP预测提供了一个具有成本效益的替代方案.

研究的目的:

  • 开发一种新的深囊神经网络模型,pNPs-CapsNet,用于准确预测神经 (NP) 和非NP.
  • 利用先进的蛋白质语言模型和特征选择策略来提高预测性能.
  • 建立一个强大的计算工具,以加速药物发现中的NP识别.

主要方法:

  • 使用预训练的蛋白质语言模型 (ESM,ProtBERT-BFD,ProtT5) 对序列的数值编码.
  • 使用差异演变来创建一个多视图向量的权重特征集成.
  • 两层特征选择 (MRMD和SHAP),然后训练一个囊神经网络 (CapsNet).

主要成果:

  • pNPs-CapsNet模型在培训数据上实现了98.10%的准确性和0.98 AUC.
  • 独立验证证明了95.21%的准确性和0.96 AUC.
  • 在培训和独立数据集方面,pNNPs-CapsNet的表现分别超过现有模型的4%和2.5%.

结论:

  • pNNPs-CapsNet是用于神经预测的高度准确和强大的计算模型.
  • 该模型在当前最先进的方法中提供了显著的进步.
  • pNNPs-CapsNet显示了促进药物发现和学术研究的巨大潜力.