在生物医学研究中数据驱动的数字双胞胎的发现
Clémence Métayer1, Annabelle Ballesta1, Julien Martinelli2
1Inserm U1331, Institut Curie, PSL Research University, CBIO-Center for Computational Biology, Mines Paris, Cancer Systems Pharmacology team, Saint-Cloud 92210, France.
Briefings in bioinformatics
|February 11, 2026
概括
自动化方法对于从复杂数据中创建生物数字双胞胎至关重要. 稀疏回归显示出有希望的结果,但结合机械模型和深度学习的混合方法是未来进步的关键.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物医学信息学 生物医学信息学
背景情况:
- 高通量生物数据使得个性化医疗的数字双胞胎开发成为可能.
- 目前的数字双胞胎创建依赖于手动数据集成,需要自动化方法.
- 与物理学相比,生物学对数据驱动的发现提出了独特的挑战.
研究的目的:
- 从时间序列数据中对生物数字双胞胎的自动推断方法进行审查和评估.
- 对关键的生物学和方法学挑战进行算法评估.
- 提出未来发展强大的数字双胞胎推理方法的方向.
主要方法:
- 对177种推断数字双胞胎的方法进行系统审查.
- 基于八个挑战的算法评估:数据集成,多个条件,先验知识,潜在变量,高维度,未观察到的变量衍生,库设计和不确定性量化.
- 稀疏回归,符号回归,深度学习和大型语言模型的比较.
主要成果:
- 稀疏回归通常优于象征回归,特别是在贝叶斯框架中.
- 深度学习和大型语言模型显示了整合先前知识的潜力,但需要提高可靠性.
- 目前没有一种单一的方法能够有效地应对所有挑战.
结论:
- 混合和模块化框架对于推动数字双胞胎发展至关重要.
- 未来的进步需要结合机械基础 (化学反应网络),贝叶斯不确定性量化和深度学习的生成能力.
- 需要标准化的基准来评估所有已识别的挑战中的方法.
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