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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.
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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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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
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Updated: Jul 19, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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在使用联合深度学习的分布式数据源上预测抗癌药物敏感性.

Xiaolu Xu1, Zitong Qi2, Xiumei Han3

  • 1School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian 116029, China.

Heliyon
|August 18, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了HFDL-fl,这是一个用于隐私保护药物敏感性预测的联合学习模型. 它使机构之间能够进行协作,提高癌症治疗的准确性,同时保护患者数据.

关键词:
深度学习是一种深度学习.药物敏感性预测 药物敏感性预测联合学习学习是联合学习.基因表达 基因表达多种类型的焦点损失.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 精准医学是一门精准的医学.

背景情况:

  • 药物敏感性预测对于个性化癌症治疗至关重要.
  • 数据隐私法规阻碍了协作预测研究.
  • 集中式模型面临着由于数据孤岛的挑战.

研究的目的:

  • 开发一个保护隐私的联合药物敏感性预测模型.
  • 实现跨医疗机构的协作,而不会影响数据保护.
  • 加强药物敏感性预测模型的泛化.

主要方法:

  • 提出了一个新的水平联合深度学习框架与焦点损失 (HFDL-fl).
  • 使用布方法将细胞系分为三个类别.
  • 该模型适用于均质 (HFDL-Within) 和异质 (HFDL-Cross) 数据.

主要成果:

  • 与私人,本地模型相比,HFDL-fl表现出优越的性能.
  • 焦点损失函数有效地提高了敏感和耐药细胞系的分类准确性.
  • 在各机构的数据异质性方面,HFDL-fl显示出强度.

结论:

  • HFDL-fl为协作药物敏感性预测提供了一个保护隐私的解决方案.
  • 这种方法克服了生物医学研究中的数据共享障碍.
  • 它促进了癌症精准医学和其他敏感数据研究的进步.