Dataset Information

Learning stable and predictive structures in kinetic systems.

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.

SUBMITTER: Pfister N

PROVIDER: S-EPMC6925987 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Learning stable and predictive structures in kinetic systems.

Pfister Niklas N Bauer Stefan S Peters Jonas J

Proceedings of the National Academy of Sciences of the United States of America 20191127 51

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 bot ...[more]

PMID: 31776252

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Dataset Information

Learning stable and predictive structures in kinetic systems.

Publications

Learning stable and predictive structures in kinetic systems.

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