一个全面的机器学习技术的综合复杂的omics数据集成的综合审查:在精密瘤学的挑战和应用
Debabrata Acharya1, Anirban Mukhopadhyay1
1Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, West Bengal, India.
Briefings in functional genomics
|April 11, 2024
概括
本综述探讨了机器学习如何在精准医学中整合多omics数据 (基因组学,转录组学,蛋白质组学,代谢组学). 它强调了患者分层的方法,特别是在精密瘤学中,并讨论了挑战和机会.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 多学科数据集成对于理解精准医学中复杂的生物相互作用至关重要.
- 机器学习 (ML) 方法越来越多地用于分析和整合各种omics数据集.
研究的目的:
- 综合审查和分类用于精准医学的多omics数据集成中的ML进步.
- 为患者分层应用ML技术提供见解,特别是在精密瘤学中.
主要方法:
- 关于将基因组学,转录组学,蛋白质组学和代谢组学与临床数据集成的ML技术的文献综述.
- 探索深度学习和基于网络的方法来协调生物信息.
主要成果:
- 识别各种ML技术和计算方法用于多omics集成.
- 讨论数据整合,精准医学和患者分层方面的挑战和机遇.
- 介绍了精密瘤学的多学科数据集成路线图.
结论:
- 机器学习为准确医学中使用多omics数据进行患者分层提供了强大的工具.
- 有效整合多学科数据是推进精确瘤学和改善患者治疗结果的关键.
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