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超越二进制:用于解释癌症诊断中的生物行为的机器学习框架.

Aya Hasan Alshammari1, Monther F Mahdi2, Takaaki Hirotsu1

  • 1Hirotsu Bioscience Inc., New Otani Garden Court 22F, 4-1 Kioi-cho, Chiyoda-ku, Tokyo 102-0094, Japan.

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概括

生物生物传感利用生物系统来检测与癌症相关的挥发性有机化合物 (VOC). 本次审查提出了一种双通道框架,以推进这些低成本,非侵入性诊断技术的临床使用.

关键词:
凯诺哈比迪斯的优雅的植物.行为表型化行为表型化.癌症的诊断 癌症的诊断深度学习是一种深度学习.机器学习是机器学习.生物生物感知生物体精确瘤学 精确瘤学挥发性有机化合物 (VOC) 是一种挥发性有机化合物.

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科学领域:

  • 生物技术是生物技术.
  • 癌症的诊断 癌症的诊断
  • 生物传感器是一种生物传感器.

背景情况:

  • 生物生物传感利用生物体的嗅觉能力来检测癌症生物标志物.
  • 这种方法为传统的癌症诊断方法提供了一个有希望的,低成本的,非侵入性的替代方案.
  • 包括线虫,犬类和昆虫在内的多种生物平台已经在检测与癌症相关的挥发性有机化合物 (VOC) 方面表现出可行性.

研究的目的:

  • 审查目前生物体生物传感用于癌症检测的现状.
  • 突出各种生物系统在识别癌症特异性VOC特征方面的潜力.
  • 提出一个新的双路径框架,以推动生物生物传感到临床应用.

主要方法:

  • 对生物体生物传感平台 (例如,C. elegans,犬类,昆虫) 进行癌症检测的现有研究的审查.
  • 分析不同生物传感模式中报告的灵敏度,特异性和准确性.
  • 引入拟议的双通道框架,将高吞吐量查和机器学习集成为先进诊断.

主要成果:

  • 在不同的诊断性能 (例如,C. elegans* 87-96%的灵敏度,狗 ~ 71%的灵敏度,昆虫 82-100%的准确度) 的不同平台上证明了生物生物传感的可行性.
  • 验证生物体行为和神经活动编码与癌症相关的VOC签名.
  • 确定局限性,包括小队列大小和方法异质性.

结论:

  • 有机生物传感平台显示出与癌症相关的VOC检测的巨大潜力.
  • 拟议的双路径框架旨在通过结合选和机器学习来克服当前的局限性.
  • 这种综合方法可以促进将生物体生物传感转化为可扩展的,精确的癌症诊断.