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.

翻译：物理信息机器学习（PIML）已成功将机理理解融入机器学习，尤其在受已知物理定律主导的领域取得了显著成效。这一成功激励了将PIML应用于生物学的尝试——该领域虽富含动力学系统，却受不同约束条件塑造。然而，生物建模面临独特挑战：多层面且不确定的先验知识、异质且噪声数据、部分可观测性，以及复杂的高维网络。在本立场论文中，我们认为这些挑战不应被视为PIML的障碍，而应成为其演进的催化剂。我们提出生物信息机器学习（BIML）：作为PIML的原则性扩展，它在保持结构基础的同时，适应生物学的实际特性。BIML并非取代PIML，而是重构其方法以在更柔性、概率化的先验知识形式下运作。我们规划了支撑这一转型的四大基础支柱作为路线图：不确定性量化、情境化、约束潜在结构推断与可扩展性。基础模型与大型语言模型将成为关键赋能工具，桥接人类专业知识与计算建模。最后，我们提出具体建议以构建BIML生态系统，并将受PIML启发的创新导向具有高度科学与社会相关性的挑战领域。