将物理启发的机器学习拓展至生物领域，应对复杂不确定性挑战。
Position: Biology is the Challenge Physics-Informed ML Needs to Evolve
- 提出生物学启发的机器学习（BIML），以概率化方式融合生物先验知识
- 强调不确定性量化、上下文建模等四条核心支柱，支撑高维生物系统建模
- 适合关注生物系统建模、可解释性与跨学科创新的研究者
物理启发的机器学习（PIML）在遵循明确物理定律的领域取得成功，推动其向生物学拓展。然而，生物系统具有多维度、不确定的先验知识，数据异质且含噪，可观测性有限，网络结构复杂且高维。本文认为这些挑战不应被视为障碍，而是推动PIML演进的催化剂。我们提出生物学启发的机器学习（BIML）：在保持结构基础的同时，适应生物学的现实约束。BIML通过软化、概率化的先验知识实现方法重构。我们提出四大支柱作为转型路线图：不确定性量化、上下文建模、受限潜在结构推断与可扩展性。基础模型和大语言模型将成为关键使能者，连接人类知识与计算建模。最后，我们提出具体建议，构建BIML生态，引导基于PIML的创新解决高科学与社会价值的问题。
原文摘要 · Abstract (English)
Physics-Informed Machine Learning (PIML) has successfully integrated mechanistic understanding into machine learning, particularly in domains governed by well-known physical laws. This success has motivated efforts to apply PIML to biology, a field rich in dynamical systems but shaped by different constraints. Biological modeling, however, presents unique challenges: multi-faceted and uncertain prior knowledge, heterogeneous and noisy data, partial observability, and complex, high-dimensional networks. In this position paper, we argue that these challenges should not be seen as obstacles to PIML, but as catalysts for its evolution. We propose Biology-Informed Machine Learning (BIML): a principled extension of PIML that retains its structural grounding while adapting to the practical realities of biology. Rather than replacing PIML, BIML retools its methods to operate under softer, probabilistic forms of prior knowledge. We outline four foundational pillars as a roadmap for this transition: uncertainty quantification, contextualization, constrained latent structure inference, and scalability. Foundation Models and Large Language Models will be key enablers, bridging human expertise with computational modeling. We conclude with concrete recommendations to build the BIML ecosystem and channel PIML-inspired innovation toward challenges of high scientific and societal relevance.
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