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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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Updated: Jun 30, 2025

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ACPPfel:基于特征优化的抗癌预测可解释的深层集体学习.

Mingyou Liu1,2, Tao Wu1, Xue Li1,2

  • 1School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China.

Frontiers in genetics
|March 15, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种深度集体学习方法,以有效预测抗癌 (ACP),为传统查提供更快的替代方案. 开发的模型实现了高精度,有助于发现新的癌症治疗方法.

关键词:
抗癌 (ACP) 是一种抗癌.深度卷积神经网络 (DCNN) 是一个深度卷积神经网络.组合学习组合学习可以解释的学习学习.功能优化优化功能优化

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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 在瘤学瘤学.

背景情况:

  • 癌症仍然是全球死亡的主要原因,常规治疗对重要器官功能构成风险.
  • 抗癌 (ACP) 由于其特异性和较低的毒性,显示出作为向癌症治疗的前景.
  • 通过传统的湿实验室方法确定有效的ACP是劳动密集型和耗时的.

研究的目的:

  • 开发一个用于预测抗癌 (ACP) 的计算框架.
  • 加快识别潜在的癌症治疗的非洲和非洲国家和地区.
  • 为抗癌发现领域的研究人员提供一个用户友好的工具.

主要方法:

  • 为ACP预测构建了一个深度集体学习模型.
  • 功能选择和维度减少技术被整合到模型培训中.
  • 用四个不同的数据集验证了模型的性能.
  • 进行了解释性分析,以确定关键的预测特征.

主要成果:

  • 深层组合模型实现了高预测准确性,在ACPfel数据集上达到98.53%的准确性和0.9972的AUC.
  • 该框架在多个数据集中表现出更好的性能.
  • 开发了一个Web服务器,以便为研究界提供可访问的ACP预测.

结论:

  • 深度集体学习方法为预测抗癌提供了一种高效准确的方法.
  • 这种计算策略大大减少了用于非洲国家和地区识别所需的时间和资源.
  • 开发的Web服务器为推进抗癌研究和药物发现提供了宝贵的资源.