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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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Early diagnosis and treatment can often cure cancer. However, even with treatment, residual cells called cancer stem cells (CSC) might remain, often causing tumor recurrence. These cancer stem cells possess the potential for self-renewal and multi-lineage differentiation and are often responsible for the therapeutic resistance displayed in most cancers.
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Cancer02:18

Cancer

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Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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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.
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通过多任务学习利用不同癌症类型的共同模式.

Bo-Run Wu1, Sofia Ormazabal Arriagada2,3,4, Te-Cheng Hsu5

  • 1Graduate Institute of Communication Engineering, National Taiwan University (NTU), Taipei, Taiwan.

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概括

深度学习通过整合RNA测序和临床数据来提高癌症预后. 这种多任务方法可以改善各种癌症类型的预测,帮助精准医学.

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科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能

背景情况:

  • 传统的癌症预后方法面临复杂的遗传生物标志物和各种医疗数据的局限性.
  • 精确识别高风险癌症患者对于改善生存结果至关重要.

研究的目的:

  • 开发一种深度学习模型,以改善癌症预后预测.
  • 通过多任务学习,探索不同癌症类型的共同模式.
  • 整合RNA测序和临床数据,以提高预测准确度.

主要方法:

  • 开发了一个多任务双模神经网络.
  • 综合RNA测序 (RNA-Seq) 和来自癌症基因组图谱 (TCGA) 数据集的临床数据 (乳腺侵入性癌,肺腺癌,结肠腺癌).
  • 使用小细胞肺癌数据进行了外部验证.

主要成果:

  • 显著改善了预后预测,特别是结肠腺癌 (共指数增加了26%,精度回忆曲线下的面积增加了41%).
  • 外部验证显示了可比的指标,表明补充小型数据集的好处.
  • 该模型有效地将高维数据蒸成低维特征向量.

结论:

  • 多任务学习在不同类型的癌症中显示出癌症预后预测的前景.
  • 这种方法可以揭示不同癌症之间共同的潜在机制.
  • 这项研究通过提高预后能力,有助于推进精准医学.