Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations

Frontiers in Physics

Gregory R. Johnson¹  Meagan P. Rowan²  Padmini Rangamani⁴  Christopher T. Lee⁴  Ritvik Vasan⁴  Michael Holst⁵ 

[1] Allen Institute of Cell Science, Seattle, WA, United States;Department of Bioengineering, University of California San Diego, La Jolla, CA, United States;Department of Mathematics, University of California San Diego, La Jolla, CA, United States;Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, United States;Department of Physics, University of California San Diego, La Jolla, CA, United States;

关键词: machine learning;    cellular structures;    segmentation;    reconstruction;    meshing;    simulation;   

DOI  :  10.3389/fphy.2019.00247

来源: DOAJ

【 摘 要 】

In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.

【 授权许可】

Unknown