ArticleChaos (Woodbury, N.Y.)2019
Analyzing collective motion with machine learning and topology.
Article in Chaos (Woodbury, N.Y.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Topological model selection: a case-study in tumour-induced angiogenesis.Bioinformatics (Oxford, England) · 2026Article
- Topology across scales on heterogeneous cell data.PLoS computational biology · 2025Article
- Spatial and Sequential Topological Analysis of Molecular Dynamics Simulations of IgG1 Fc Domains.Journal of chemical theory and computation · 2025Article
- Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling.Mathematical biosciences · 2024Article
- Topological data analysis of spatial patterning in heterogeneous cell populations: clustering and sorting with varying cell-cell adhesion.NPJ systems biology and applications · 2023Article
- Zigzag persistence for coral reef resilience using a stochastic spatial model.Journal of the Royal Society, Interface · 2023Article
- Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis.PLoS computational biology · 2021Article
- Topological data analysis of collective and individual epithelial cells using persistent homology of loops.Soft matter · 2021Article
- Minimal Cycle Representatives in Persistent Homology Using Linear Programming: An Empirical Study With User's Guide.Frontiers in artificial intelligence · 2021Article
- Topology Applied to Machine Learning: From Global to Local.Frontiers in artificial intelligence · 2021Review
- Bridging from single to collective cell migration: A review of models and links to experiments.PLoS computational biology · 2020Review
- Examination of an averaging method for estimating repulsion and attraction interactions in moving groups.PloS one · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D'Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based on topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters.
Identifiers
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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.