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相关概念视频

Treatment Resistant Cancers02:56

Treatment Resistant Cancers

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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 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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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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相关实验视频

Updated: Sep 12, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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使用REFINED CNN进行抗癌药物敏感性的预测建模.

Daniel Nolte1, Omid Bazgir2, Ranadip Pal3

  • 1Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.

Methods in molecular biology (Clifton, N.J.)
|August 8, 2025
PubMed
概括

本研究介绍了REFINED,这是一种将表格数据转换为图像的方法,用于使用卷积神经网络 (CNN) 改进抗癌药物敏感性预测. REFINED通过利用CNN对图像格式的特征来提高预测性能.

关键词:
卷积神经网络是一种卷积神经网络.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

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 机器学习是机器学习.

背景情况:

  • 卷积神经网络 (CNN) 在空间数据方面表现出色,但在缺乏内在结构的表格式数据集方面扎.
  • 预测抗癌药物敏感性通常使用表格数据,限制了CNN的应用.
  • 现有的方法可能无法充分利用非顺序,非图像数据中的特征关系.

研究的目的:

  • 介绍一个新的计算程序,特征作为图像与邻里依赖 (REFINED) 的特征再表示.
  • 为了使CNN能够有效地应用于表格数据集,用于诸如抗癌药物敏感性预测等任务.
  • 通过创建高维特征向量的图像样表示来提高预测性能.

主要方法:

  • 开发了REFINED以将高维特征向量映射到紧的2D图像中.
  • 集成REFINED与CNN模型进行深度学习应用.
  • 在高维特征向量上,将CNN与REFINED与完全连接的网络进行了比较.

主要成果:

  • REFINED成功地将表格数据转换为适合CNN的图像格式.
  • 通过REFINED-CNN方法,预测性能得到了提高.
  • 与传统方法相比,观察到模型参数化的减少和嵌入式特征提取的改进.

结论:

  • REFINED提供了一个可行的策略,以适应CNN用于表格数据分析.
  • 这种方法在抗癌药物敏感性预测中提供了更高的准确性.
  • 在深度学习模型中,REFINED可以更好地利用复杂的生物数据集.