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XCPP:一个多模型可解释的深度学习框架,用于从结构化序列特征准确识别穿透细胞的.

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概括

深度学习模型准确地预测细胞透 (CPPs),对于药物输送和诊断至关重要. 卷积神经网络 (CNN) 显示出卓越的性能,SHAP分析提高了模型的解释性.

关键词:
深度学习是一种深度学习.生物信息学是一种生物信息学.可以解释的人工智能 (XAI).

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 药物输送系统 药物输送系统

背景情况:

  • 细胞透 (CPP) 是短的氨基酸序列,使治疗分子能够通过细胞膜传输.
  • CPP提供了一个针对药物输送和分子诊断的多功能平台.

研究的目的:

  • 开发和评估深度学习模型,以准确地预测CPPs的in silico.
  • 使用可解释AI (XAI) 识别有助于CPP活动的关键序列特征.

主要方法:

  • 从EnDM-CPP数据库中分析了473个已确认的CPP.
  • 四个序列描述符 (PRIM,RPRIM,AAPIV,反向AAPIV) 的计算.
  • 深度神经网络 (DNN),卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 模型的培训和测试.
  • 模型评估使用自我一致性,独立测试和十倍交叉验证.
  • 应用SHAP值用于XAI来解释模型预测.

主要成果:

  • 在交叉验证过程中,CNN模型获得了最高的准确性 (99.05%),超过了DNN和LSTM模型.
  • 所有模型都表现出了合理的预测准确性与结构化输入特征.
  • SHAP分析成功地确定了生物相关的序列描述符,提高了模型的透明度.

结论:

  • 深度学习,特别是CNN,为准确的CPP识别提供了有效的框架.
  • 这项研究强调了 in silico CPP 预测在药物输送,诊断和个性化医学的应用方面的潜力.
  • 基于SHAP的XAI通过将序列特征与生物特性联系起来,增加了对模型预测的信心.