biostudies-literatureUnknown122(3)Baglaeva IScientific Grant Agency of the MŠVVaŠ and the SAVAgentúra Ministerstva Školstva, Vedy, Výskumu a Športu SRSlovak Research and Development AgencySlovak Academy of SciencesDynamic systems such as cells or tissues generate, either spontaneously or in response to stimuli, transient signals that carry information about the system. Characterization of recorded transients is often hampered by a low signal-to-noise ratio (SNR). Reduction of the noise by filtering has limited use due to partial signal distortion. Occasionally, transients can be approximated by a mathematical function, but such a function may not hold correctly if recording conditions change. We introduce here the model-independent approximation method for general noisy transient signals based on the Gaussian process regression. The method was implemented in the software TransientAnalyzer, which detects transients in a record, finds their best approximation by the Gaussian process, constructs a surrBiophysical journal451-459https://www.ebi.ac.uk/biostudies/studies/S-EPMC9941716biostudies-literatureAnalysis of noisy transient signals based on Gaussian process regression.PMC9941716Iaparov BZahradnikova AZahradnik IBaglaeva IfalseAnalysis of noisy transient signals based on Gaussian process regression.Dynamic systems such as cells or tissues generate, either spontaneously or in response to stimuli, transient signals that carry information about the system. Characterization of recorded transients is often hampered by a low signal-to-noise ratio (SNR). Reduction of the noise by filtering has limited use due to partial signal distortion. Occasionally, transients can be approximated by a mathematical function, but such a function may not hold correctly if recording conditions change. We introduce here the model-independent approximation method for general noisy transient signals based on the Gaussian process regression. The method was implemented in the software TransientAnalyzer, which detects transients in a record, finds their best approximation by the Gaussian process, constructs a surr2023-01-01T00:00:00Z2023 Feb2025-04-22T05:37:02.796Z2025-04-05T21:23:30.996ZS-EPMC99417163660914110.1016/j.bpj.2023.01.003