Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations
Biological Physics
2019-11-14 v1 Image and Video Processing
Computational Physics
Abstract
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.
Cite
@article{arxiv.1911.05218,
title = {Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations},
author = {Ritvik Vasan and Meagan P. Rowan and Christopher T. Lee and Gregory R. Johnson and Padmini Rangamani and Michael Holst},
journal= {arXiv preprint arXiv:1911.05218},
year = {2019}
}