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EnDM-CPP:基于深度学习和机器学习的多视图可解释框架,用于识别细胞透的转换器和分析序列信息.

Lun Zhu1, Zehua Chen1, Sen Yang2,3

  • 1School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, 213164, China.

Interdisciplinary sciences, computational life sciences
|December 23, 2024
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概括
此摘要是机器生成的。

计算方法可以预测细胞透 (CPPs) 用于药物输送,减少合成时间. EnDM-CPP模型有效地识别了潜在的CPP,使用融合功能和先进的机器学习,改善治疗开发.

关键词:
细胞透的.的内在特征是的内在特征.堆叠模型的堆叠模型基于变压器的功能.

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科学领域:

  • 生物技术和制药科学 生物技术和制药科学
  • 计算生物学和化学信息学

背景情况:

  • 细胞透 (CPP) 对于药物输送至关重要.
  • 实验室合成CPP需要大量的时间和资源.
  • 对CPP的计算预测可以加速治疗的发展.

研究的目的:

  • 开发一种准确的计算方法来预测细胞透 (CPPs).
  • 为了提高治疗应用中CPP发现的效率.

主要方法:

  • 开发了一个混合模型,EnDM-CPP,集成支持矢量机器 (SVM),CatBoost,卷积神经网络 (CNN) 和TextCNN.
  • 来自CPPsite 2.0,MLCPP 2.0和CPP924的数据集被合并,以改善多样性和减少同质性.
  • 使用了基于变压器的特征 (ProtT5,ESM-2) 和序列特征 (CPRS,混合PseAAC,KSC).
  • 后勤回归 (LR) 用于基于单个模型输出的最终决策预测.

主要成果:

  • 来自ProtT5和ESM-2的融合特性显著提高了CPP预测的准确性.
  • 与单个模型相比,组合模型表现出优越的性能.
  • 在一个独立的测试组中,EnDM-CPP实现了0.9495准确度和0.9008马修斯相关系数.
  • 性能改进在准确度上为2.23% - 9.48%,在MCC中比最先进的方法提高4.32% - 19.02%.

结论:

  • EnDM-CPP提供了一个高度准确和高效的计算方法来识别细胞透.
  • 该研究强调了将多种功能和机器学习模型用于CPP预测的有效性.
  • 开发的方法可以加速用于药物输送应用的CPP的发现和开发.