针对耐放射性直肠癌患者的基因组驱动的营养干预措施
Joshua Southern1, Guadalupe Gonzalez1,2, Pia Borgas3
1Department of Computing, Imperial College London, London, SW7 2BX, UK.
Scientific reports
|September 8, 2023
概括
这项研究使用机器学习来寻找可能改善直肠癌患者放射治疗反应的食物化合物. 它还创建了用这些化合物丰富的配方,以获得更好的治疗结果.
科学领域:
- 在瘤学瘤学.
- 营养科学 营养科学
- 生物信息学是一种生物信息学.
背景情况:
- 在直肠癌中,放射治疗的反应是复杂的,涉及应激反应,细胞死亡和新陈代谢.
- 脂质代谢调节是一种有前途的策略,可以通过饮食中的生物活性分子来提高放射治疗的结果.
- 实验识别所有潜在的射电响应调节器是不可行的.
研究的目的:
- 开发一种机器学习 (ML) 方法来识别调节放射治疗反应的生物活性食物分子.
- 设计植物化学丰富的配方,以改善直肠癌患者的放射治疗结果.
- 建立基因组驱动的营养干预措施的管道.
主要方法:
- 用基因组驱动的网络ML,度量学习和域名知识来识别潜在的射电响应调节器.
- 使用 Recipe1M 数据库来优化食谱.
- 进行了成分替代,以最大限度地提高预测的调节器,同时保持品质.
主要成果:
- 从生物活性食品分子中识别潜在的放射反应调节器.
- 开发采用这些调制器的优化配方.
- 对设计营养干预措施的计算方法的演示.
结论:
- 机器学习可以有效地识别食品衍生化合物,以增强放射治疗反应.
- 富含植物化学物质的配方提供了一种新的策略,可以改善直肠癌患者的治疗结果.
- 这项工作使得针对癌症治疗的营养干预措施的设计成为可能.
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