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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.
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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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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基于机器学习的方法用于使用微生物组数据进行癌症预测.

Pedro Freitas1,2, Francisco Silva3,4, Joana Vale Sousa3,5

  • 1INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, 4200-465, Porto, Portugal. pedro.g.freitas@inesctec.pt.

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此摘要是机器生成的。

机器学习模型可以从微生物组数据中识别癌症类型,对结肠癌具有很高的准确性. 由于微生物的相似性,区分邻近的癌症,如食道癌和直肠癌仍然具有挑战性.

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科学领域:

  • 微生物组研究的研究.
  • 计算生物学是一种计算生物学.
  • 在瘤学瘤学.

背景情况:

  • 越来越多的证据将微生物组合与包括癌症在内的各种疾病联系在一起.
  • 在DNA测序方面的进步需要复杂的分析工具来进行微生物组研究.
  • 人类微生物组具有作为癌症鉴定预测信息的潜力.

研究的目的:

  • 开发一种机器学习 (ML) 方法来对癌症类型进行分类,使用特定组织的微生物数据.
  • 评估人类微生物组在癌症识别中的预测能力.
  • 评估不同癌症类型和解剖部位的ML模型性能.

主要方法:

  • 随机森林算法用于分类.
  • 这项研究重点关注五种癌症类型:头,食道,胃,结肠和直肠.
  • 数据来源于癌症微生物群图谱数据库,进行了一对所有和多类分析.

主要成果:

  • ML模型在头部和部,胃和结肠癌 (结肠癌精度>90%) 中显示出有希望的性能.
  • 随着癌症部位的解剖学接近度的增加,分类准确度下降.
  • 难以区分食道癌和直肠癌,以及结肠癌和直肠癌,证明了这一点.

结论:

  • 使用ML进行组织特异性微生物组分析显示了癌症检测和预防的潜力.
  • 邻近癌症中的微生物相似性对准确的分类构成了挑战.
  • 进一步开发用于微生物组数据的ML工具可以帮助减少疾病负担.