ArticleMachine learning: science and technology2025
Generative diffusion model surrogates for mechanistic agent-based biological models.
Article in Machine learning: science and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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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
6 citing papers in PubMed.
- From genes to germ layers: virtual twins of gastruloids.NPJ systems biology and applications · 2026Review
- Multiscale bone remodeling in COVID-19: from osteoimmune signaling to structural and mechanical impairment.Frontiers in bioengineering and biotechnology · 2026Review
- Generative diffusion model surrogates for mechanistic agent-based biological models.Machine learning: science and technology · 2025Article
- Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture.PLoS computational biology · 2025Article
- Article
- Polygrammar: Grammar for Digital Polymer Representation and Generation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
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6 authors.
Funding
Abstract
Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate
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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 OpenQuestion graph.