Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations

Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations

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...

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Bibliographic Details
Main Authors: Ritvik Vasan, Meagan P. Rowan, Christopher T. Lee, Gregory R. Johnson, Padmini Rangamani, Michael Holst
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-01-01
Series:Frontiers in Physics
Subjects:
machine learning
cellular structures
segmentation
reconstruction
meshing
simulation
Online Access:https://www.frontiersin.org/article/10.3389/fphy.2019.00247/full
Description
Summary: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.
ISSN:2296-424X

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