ArticleFrontiers in physics2020
Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations.
Article in Frontiers in physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Construction and simulation-experimental characterization of a fluorescence image-based finite element model for cell mechanics.Frontiers in bioengineering and biotechnology · 2026Article
- The role of molecular diffusion within dendritic spines in synaptic function.The Journal of general physiology · 2021Review
- Cell shape regulates subcellular organelle location to control early CaScientific reports · 2020Article
- 3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.