可解释的人工智能用于多模式癌症分析:从基因组学到免疫学
1Department of Mechanical and Electronic Engineering of Shangdong Management University, Ji'nan 250357.
Critical reviews in oncology/hematology
|November 25, 2025
概括
多模式深度学习整合了各种癌症数据,以便更好地预测. 可解释的人工智能增强了对瘤复杂性和免疫相互作用的理解,推进了精确瘤学.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 癌症是一种复杂,异质的疾病,在预后和治疗预测方面存在挑战.
- 单一模式的方法未能捕捉到癌症的全部生物景观.
- 多因素复杂性源于瘤与微环境的相互作用,免疫调节和治疗压力.
研究的目的:
- 审查选择性多式模式深度学习 (MDL) 在精密瘤学的应用.
- 突出不同生物医学数据的整合,以改善癌症分析.
- 强调可解释AI (XAI) 在理解复杂癌症模型中的作用.
主要方法:
- 整合补充的生物医学数据 (基因组学,转录组学,成像学,电子健康记录等). 使用MDL.
- 基于机制的融合策略,以捕捉跨规模的依赖关系和新兴模式.
- 可解释AI (XAI) 的应用,以实现透明的,基于生物学的模型解释.
主要成果:
- MDL模型可以捕捉复杂的跨度依赖关系和癌症中新出现的模式.
- 免疫学知情整合增强生物标志物发现和免疫治疗分层.
- 对于复杂的计算预测,XAI提供了基于生物学的解释.
结论:
- 选择性MDL为个性化的精密瘤学提供了一个变革性的框架.
- 严格的验证,包括统计指标和生物可信性,至关重要.
- 未来的方向包括联合学习,因果推理和数字双胞胎,以推进个性化癌症护理.
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