arXiv:2510.25368v1 Announce Type: new 
Abstract: 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.

تسلط دراسة حديثة الضوء على إمكانيات التعلم الآلي المستند إلى الفيزياء (PIML) في إحداث ثورة في نمذجة البيولوجيا. بينما ازدهر PIML في الفيزياء، فإن تطبيقه على البيولوجيا يقدم تحديات فريدة بسبب تعقيد وعدم يقين الأنظمة البيولوجية. هذه الأبحاث مهمة لأنها تفتح آفاقًا جديدة لفهم العمليات البيولوجية، مما قد يؤدي إلى تقدم في الرعاية الصحية والعلوم البيئية.

Un estudio reciente destaca el potencial del Aprendizaje Automático Informado por la Física (PIML) para revolucionar la modelización biológica. Aunque el PIML ha prosperado en la física, aplicarlo a la biología presenta desafíos únicos debido a la complejidad y la incertidumbre de los sistemas biológicos. Esta investigación es significativa ya que abre nuevas avenidas para comprender los procesos biológicos, lo que podría llevar a avances en la atención médica y la ciencia ambiental.

Une étude récente met en lumière le potentiel de l'apprentissage automatique informé par la physique (PIML) pour révolutionner la modélisation biologique. Bien que le PIML ait prospéré en physique, son application à la biologie présente des défis uniques en raison de la complexité et de l'incertitude des systèmes biologiques. Cette recherche est significative car elle ouvre de nouvelles voies pour comprendre les processus biologiques, ce qui pourrait conduire à des avancées dans les soins de santé et la science environnementale.

A recent study highlights the potential of Physics-Informed Machine Learning (PIML) to revolutionize biological modeling. While PIML has thrived in physics, applying it to biology presents unique challenges due to the complexity and uncertainty of biological systems. This research is significant as it opens new avenues for understanding biological processes, which could lead to advancements in healthcare and environmental science.

Position: Biology is the Challenge Physics-Informed ML Needs to Evolve

One More Thing in AI – Your Shortcut to AI Mastery

Position: Biology is the Challenge Physics-Informed ML Needs to Evolve

Was this article worth reading? Share it

One More Thing in AI

LucidQuery AI

FastML

Dynamiq

Pizi AI

Zemith-3bda3b

Ready to build your own newsroom?