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- W1597588404 abstract "This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data." @default.
- W1597588404 created "2016-06-24" @default.
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- W1597588404 date "2015-07-01" @default.
- W1597588404 modified "2023-10-15" @default.
- W1597588404 title "An Information-Based Approach to Change-Point Analysis with Applications to Biophysics and Cell Biology" @default.
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- W1597588404 doi "https://doi.org/10.1016/j.bpj.2015.05.038" @default.
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