在癌症知识发现中采用集成型多学科和机器学习/深度学习方法:范围审查
Nada Benabbou1, Mounia Abik1, Shakuntala Baichoo2
1Software Engineering Department, Ensias, Mohammed V University, Rabat, Morocco.
Cancer treatment and research communications
|February 25, 2026
概括
人工智能 (AI) 在癌症研究中,使用机器学习 (ML) 和深度学习 (DL) 与多组数据,显示出希望. 人工智能集成方法的标准化对于更好的癌症预测和亚型化是必要的.
科学领域:
- 瘤学和计算生物学
- 人工智能在医学中的应用
- 生物信息学和数据科学
背景情况:
- 人工智能的应用,特别是ML和DL,正在迅速推进癌症研究和数据分析.
- 整合多组学数据 (基因组学,转录组学,蛋白质组学) 为了解癌症复杂性提供了一个全面的方法.
- 越来越需要评估当前在瘤学中整合多学科的方法.
研究的目的:
- 进行范围审查,评估ML/DL方法在癌症研究的多学科整合中的有效性.
- 描述AI的模型,方法和应用,从多组数据中发现新的癌症见解.
- 识别关键主题,知识差距和关于多学科融合的文献趋势.
主要方法:
- 在70项研究中进行系统的文献搜索,重点关注癌症研究中的多组学整合.
- 对描述的模型和用于人工智能驱动的预测,分类和分类任务的方法进行分析.
- 在包括的研究中识别和综合共同的主题,设计的多样性和数据类型.
主要成果:
- 多组学集成在预测预后/诊断变量和分类癌症亚型方面发挥着至关重要的作用.
- 观察到研究设计和数据类型的显著多样性,阻碍了方法的直接比较.
- 在多组学中ML/DL的应用有助于更深入地了解癌症生物学和潜在的治疗策略.
结论:
- 由人工智能驱动的多组学集成对于促进癌症的理解,预测和分类至关重要.
- 目前的文献显示,需要在多经济学集成方法方面进行更大的标准化.
- 进一步的研究和标准化对于强大的AI驱动的瘤学临床应用至关重要.
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