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Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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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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在抗癌中增强特征提取的对比学习.

Byungjo Lee1, Dongkwan Shin1,2

  • 1Research Institute, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea.

Briefings in bioinformatics
|May 10, 2024
PubMed
概括

这项研究引入了一种深度学习模型,用于仅使用序列选抗癌 (ACP). 先进的模型显示性能有所改善,增强了基于的癌症治疗的潜力.

科学领域:

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

背景情况:

  • 癌症仍然是全球主要的死亡原因,需要新的治疗策略.
  • 抗癌 (ACP) 是有前途的,但它们的查资源密集.
  • 在形预测工具提供了一个有效的替代方案,用于识别潜在的ACP.

研究的目的:

  • 开发和评估一种深度学习模型,用于在中选抗癌 (ACP).
  • 通过使用对比学习和双编码器来提高ACP预测的准确性.
  • 对基准数据集的现有最先进方法进行模型性能评估.

主要方法:

  • 设计了一种深度学习模型,仅基于序列来预测ACP.
  • 实施了对比式学习技术,以提高模型性能.
  • 在对比学习框架内,两个独立的编码器取代了传统的数据增强方法.

主要成果:

  • 拟议的模型在六个基准数据集中的五个数据集上表现出卓越的性能.
  • 对比式学习显著优于仅在二进制分类损失上训练的模型.
  • 双编码器的使用被证明是有效的,可能取代数据增强.
关键词:
抗癌是一种抗癌.相反的学习学习学习.深度学习是一种深度学习.治疗药物查 治疗药物查

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结论:

  • 开发的深度学习模型提供了一种有效和准确的查抗癌的方法.
  • 像对比学习这样的先进的计算方法可以显著提高非洲和非洲国家和地区识别工具的预测能力.
  • 这些进展有望加速开发基于的癌症治疗方法,并改善患者的治疗结果.