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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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pACP-HybDeep:使用基于二进制树生长的变压器和结构特征编码与深度混合学习预测抗癌.

Shahid1, Maqsood Hayat2, Wajdi Alghamdi3

  • 1Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan.

Scientific reports
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概括
此摘要是机器生成的。

这项研究介绍了pACP-HybDeep,这是一种用于预测抗癌的新型计算模型. 该模型实现了高精度,为癌症药物发现和开发提供了可靠的工具.

关键词:
抗癌是一种抗癌.二进制树的生长特征选择选择二进制树的生长特征选择深度混合神经网络的神经网络.物理化学特性 物理化学特性变压器编码器编码器

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科学领域:

  • 计算生物学 计算生物学
  • 生物技术是生物技术.
  • 药物发现 药物发现 药物发现

背景情况:

  • 癌症是一个全球性的健康挑战,死亡率很高.
  • 传统的癌症治疗有其局限性,包括成本和副作用.
  • 抗癌提供有针对性的作用,副作用最小.

研究的目的:

  • 开发一个高度可靠和有效的计算模型来预测抗癌.
  • 解决现有的癌症治疗方法的局限性.
  • 帮助学术界和制药药物设计的研究人员.

主要方法:

  • 使用基于注意力的ProtBERT-BFD编码器对的数值编码语义特征.
  • 整合基于CTDT的结构信息.
  • 使用基于k-最近邻近的二进制树生长 (BTG) 算法的特征选择.
  • 使用基于CNN+RNN的深度学习模型进行培训.

主要成果:

  • 该pACP-HybDeep模型实现了95.33%的训练精度和0.97.97%的AUC.
  • 独立数据集验证结果的准确率为94.92% (Ind-S1),92.26% (Ind-S2) 和91.16% (Ind-S3).这些数据集的准确率均为94.92% (Ind-S1),92.26% (Ind-S2) 和91.16% (Ind-S3).
  • 该模型在测试数据集上显示出高效率和可靠性.

结论:

  • pACP-HybDeep模型是预测抗癌的有价值和可靠的工具.
  • 这种计算方法可以显著推进癌症药物发现.
  • 该模型的性能支持其在制药研发中的应用.