Learning stable and predictive structures in kinetic systems
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Learning stable and predictive structures in kinetic systems. / Pfister, Niklas; Bauer, Stefan; Peters, Jonas.
In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 116, No. 51, 2019, p. 25405-25411.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Learning stable and predictive structures in kinetic systems
AU - Pfister, Niklas
AU - Bauer, Stefan
AU - Peters, Jonas
PY - 2019
Y1 - 2019
N2 - 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
AB - 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
KW - kinetic systems
KW - causal inference
KW - stability
KW - invariance
KW - structure learning
U2 - 10.1073/pnas.1905688116
DO - 10.1073/pnas.1905688116
M3 - Journal article
C2 - 31776252
VL - 116
SP - 25405
EP - 25411
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
SN - 0027-8424
IS - 51
ER -
ID: 233585851