Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations

Autor:	Vasan, Ritvik, Rowan, Meagan P., Lee, Christopher T., Johnson, Gregory R., Rangamani, Padmini, Holst, Michael
Rok vydání:	2019
Předmět:	Physics - Biological Physics Electrical Engineering and Systems Science - Image and Video Processing Physics - Computational Physics
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.05218 Zobrazit plný text záznamu View this record from Arxiv