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相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

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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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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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mTOR Signaling and Cancer Progression03:03

mTOR Signaling and Cancer Progression

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The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
The mTOR pathway or the...
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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
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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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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.
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相关实验视频

Updated: Sep 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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预测基于超图表表示学习的抗癌药物反应.

Wei Peng, Xinyue Xu, Jiangzhen Lin

    IEEE transactions on computational biology and bioinformatics
    |August 14, 2025
    PubMed
    概括

    这项研究引入了HRLCDR,这是一个新的计算框架,通过分析高阶相互作用来预测癌症药物反应. HRLCDR改进了现有方法,为个性化癌症疗法提供了更准确的预测.

    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.
    • 机器学习在瘤学中的应用

    背景情况:

    • 个性化癌症治疗依赖于准确的药物反应预测.
    • 当前的图形神经网络 (GNN) 方法往往忽视了细胞系和药物之间的复杂,高阶相互作用.
    • 需要先进的计算框架来捕捉这些复杂的关系.

    研究的目的:

    • 开发和评估HRLCDR,这是一个新的超图形表示学习框架,用于预测癌症药物反应.
    • 为了提高预测准确性,利用更高阶的相互作用.
    • 提高精密瘤学计算模型的可靠性.

    主要方法:

    • 构建细胞系和药物超图,并应用超图卷积来提取特征.
    • 从已知的细胞系药物反应构建异质图形,并使用图形卷积.
    • 整合来自超图和异质图分析的特征,用于使用分类器预测药物反应.

    主要成果:

    • HRLCDR有效地从更高阶交互中提取共同和独特的特征.
    • 该框架在GDSC和CCLE数据集上表现出优越的性能,与最先进的方法相比.
    • 在提高癌症药物反应预测的准确性方面,HRLCDR显示出显著的潜力.

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    Last Updated: Sep 11, 2025

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    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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    结论:

    • HRLCDR提供了一种强大的方法来模拟复杂的相互作用,用于药物反应预测.
    • 该框架推动了计算瘤学和个性化医学领域的发展.
    • HRLCDR捕捉高阶交互的能力是提高预测性能的关键.