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
Hafsah Riasat1, Tamim Alkhalifah2, Fahad Alturise3
1Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.
Current drug targets
|January 27, 2026
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.
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.
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