Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data

    Biophysical Journal, pp. 3196-3205, 2009.

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    artificial intelligencerate constantmaximum likelihoodtime series datatime seriesMore(9+)

    Abstract:

    Time series data provided by single-molecule Förster resonance energy transfer (smFRET) experiments offer the opportunity to infer not only model parameters describing molecular complexes, e.g., rate constants, but also information about the model itself, e.g., the number of conformational states. Resolving whether such states exist or ho...More

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