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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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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
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Updated: May 20, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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基于深度转移学习的癌症药物敏感性预测.

Weijun Meng1, Xinyu Xu2, Zhichao Xiao2

  • 1School of Computer Science and Technology, Xi'an University of Posts & Telecommunications, Xi'an 710071, China.

International journal of molecular sciences
|March 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个深度转移学习模型,用于在不同数据库中预测药物易感性. 该模型整合了癌症细胞系基因组学和化合物化学,使精确的药物开发和个性化医学策略成为可能.

关键词:
深度转移学习 (deep transfer learning) 是一种深度转移学习.适应领域的方法.药物敏感性 药物敏感性多个来源的数据数据.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 药物基因组学 药物基因组学
  • 药物发现 药物发现

背景情况:

  • 现型查对于药物发现至关重要,但由于分布差异,整合多种药物敏感性数据具有挑战性.
  • 现有的计算方法很难有效地利用多来源的药物基因组学数据来预测药物易感性.

研究的目的:

  • 开发一种深度转移学习模型,用于在异质数据库中准确预测药物易感性.
  • 为了应对药物基因组学数据分析中跨数据库分布差异的挑战.
  • 创建一个可靠的计算工具,用于精密药物开发和个性化医疗.

主要方法:

  • 癌症细胞系的综合基因组特征与化合物化学信息.
  • 使用了癌症细胞系百科全书 (CCLE) 和癌症药物敏感性基因组学 (GDSC) 数据集.
  • 采用域调整的深度转移学习方法来预测半最大抑制度 (IC50值).

主要成果:

  • 通过整合多来源的异质数据,成功预测了药物敏感性 (IC50值).
  • 验证了拟议的深度转移学习模型的预测准确性.
  • 证明了该模型克服跨数据库分布挑战的能力.

结论:

  • 开发的深度转移学习模型有效地预测了药物易感性,促进了精确的药物开发.
  • 这种方法可以优化个性化医学的治疗策略.
  • 该模型为高通量药物查和新药标发现提供技术支持.