Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

中文翻译：

---

整合机器学习和多尺度建模-生物学，生物医学和行为科学领域的观点，挑战和机遇。

在突破性技术发展的推动下，生物，生物医学和行为科学现在正在收集比以往更多的数据。迫切需要一种节省时间和成本的策略来分析和解释这些数据，以改善人类健康。机器学习作为集成多模态，多保真度数据并揭示相互交织的现象之间的相关性的强大技术的最新兴起在这方面提供了特殊的机会。但是，仅机器学习就忽略了物理的基本定律，并可能导致不适定的问题或非物理的解决方案。多尺度建模是整合多尺度，多物理场数据和揭示解释功能出现的机制的成功策略。然而，单凭多尺度建模常常无法有效地组合来自不同来源和不同分辨率级别的大型数据集。在这里，我们证明了机器学习和多尺度建模可以自然地相互补充，以创建强大的预测模型，该模型可以集成基础物理来管理不适当地的问题并探索庞大的设计空间。我们回顾了当前的文献，重点介绍了应用和机会，解决了未解决的问题，并讨论了四个总体主题领域中的潜在挑战和局限性：常微分方程，偏微分方程，数据驱动的方法和理论驱动的方法。为了实现这些目标，我们利用了应用数学，计算机科学，计算生物学，生物物理学，生物力学，工程力学，实验，和医学。我们的多学科观点表明，将机器学习与多尺度建模相结合可以提供对疾病机制的新见解，帮助确定新的目标和治疗策略，并为人类健康造福决策。