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• W3179508651 abstract "Robust analysis of signals from stochastic biomolecular processes is critical for understanding the dynamics of biological systems. Measured signals typically show multiple states with heterogeneities and a wide range of state lifetimes. Here, we present an algorithm for robust detection of state transitions in experimental time traces where the properties of the underlying states are a priori unknown. The method implements a maximum-likelihood approach to fit models in neighboring windows of data points. Multiple windows are combined to achieve a high sensitivity for state transitions with a wide range of lifetimes. The proposed maximum-likelihood multiple-windows change point detection (MM-CPD) algorithm is computationally extremely efficient and enables real-time signal analysis. By analyzing both simulated and experimental data, we demonstrate that the algorithm provides accurate change point detection in time traces with multiple heterogeneous states that are a priori unknown. A high sensitivity for a wide range of state lifetimes is achieved." @default.
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• W3179508651 date "2021-07-01" @default.
• W3179508651 modified "2023-09-24" @default.
• W3179508651 title "Real-Time Detection of State Transitions in Stochastic Signals from Biological Systems" @default.
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• W3179508651 doi "https://doi.org/10.1021/acsomega.1c02498" @default.
• W3179508651 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8280633" @default.
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