ArticleProceedings of the National Academy of Sciences of the United States of America2019
Learning stable and predictive structures in kinetic systems.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. 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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Who cites it
6 citing papers in PubMed.
- Generative models of cell dynamics: from Neural ODEs to flow matching.Communications biology · 2026Review
- Generalism drives abundance: A computational causal discovery approach.PLoS computational biology · 2022Article
- Article
- Kernel Ordinary Differential Equations.Journal of the American Statistical Association · 2022Article
- Physics-informed learning of governing equations from scarce data.Nature communications · 2021Article
- Learning stable and predictive structures in kinetic systems.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
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Authors and funding
3 authors.
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Abstract
Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from discrete time, noisy observations, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying, invariant kinetic model, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well-established approaches focusing solely on predictive performance, especially for out-of-sample generalization.
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