Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are trained in a supervised learning framework to approximate in vitro cell biology assay experiments while respecting a generalized form of the governing reaction-diffusion partial differential equation (PDE). By allowing the diffusion and reaction terms to be multilayer perceptrons (MLPs), the nonlinear forms of these terms can be learned while simultaneously converging to the solution of the governing PDE. Further, the trained MLPs are used to guide the selection of biologically interpretable mechanistic forms of the PDE terms which provides new insights into the biological and physical mechanisms that govern the dynamics of the observed system. The method is evaluated on sparse real-world data from wound healing assays with varying initial cell densities [2].
Massachusetts Institute of Technology, Computational and Systems Biology Program（麻省理工学院计算与系统生物学计划）；Broad Institute of MIT and Harvard（MIT和哈佛大学博德研究所）；Massachusetts Institute of Technology, Department of Biological Engineering（麻省理工学院生物工程系）
Integration of Clinical, Biological, and Computational Perspectives to Support Cerebral Autoregulatory Informed Clinical Decision Making Decomposing Cerebral Autoregulation using Mechanistic Timescales to Support Clinical Decision-Making
Sparsity is All You Need: Rethinking Biological Pathway-Informed Approaches in Deep Learning
Computational Biomedicine Unit, Department of Medical Sciences, University of Torino（托斯尼亚大学医学科学系计算生物医学单元）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）
Learning dynamical systems with biochemically informed neural ordinary differential equations
Laboratory for Systems Medicine, Department of Medicine, University of Florida（系统医学实验室，医学系，佛罗里达大学）；Department of Computational Science and Philosophy, Frankfurt School of Finance and Management（计算科学与哲学系，法兰克福金融与管理学院）
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院）；First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院）
Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks
Georgia Institute of Technology（佐治亚理工学院）；Helmholtz Zentrum Munich（慕尼黑海德堡研究中心）；Technische Universität München（慕尼黑技术大学）；Emory University School of Medicine（埃默里大学医学院）；The Wallace H. Coulter Department of Biomedical Engineering（沃克生物医学工程系）
Physics-Informed Neural Networks for Biological $2\mathrm{D}{+}t$ Reaction-Diffusion Systems
Dept. of Information Technology（信息科技系）；Uppsala University（乌普萨拉大学）；Dept. of Medical Genetics（医学遗传学系）；Oslo University Hospital（奥斯陆大学医院）；Dept. of Mathematics and Statistics（数学与统计学系）；The College of New Jersey（新泽西学院）
