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20 April 2024 |
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Article overview
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Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data | Jan-Willem van de Meent
; Jonathan E. Bronson
; Frank Wood
; Ruben L. Gonzalez Jr.
; Chris H. Wiggins
; | Date: |
15 May 2013 | Abstract: | We address the problem of analyzing sets of noisy time-varying signals that
all report on the same process but confound straightforward analyses due to
complex inter-signal heterogeneities and measurement artifacts. In particular
we consider single-molecule experiments which indirectly measure the distinct
steps in a biomolecular process via observations of noisy time-dependent
signals such as a fluorescence intensity or bead position. Straightforward
hidden Markov model (HMM) analyses attempt to characterize such processes in
terms of a set of conformational states, the transitions that can occur between
these states, and the associated rates at which those transitions occur; but
require ad-hoc post-processing steps to combine multiple signals. Here we
develop a hierarchically coupled HMM that allows experimentalists to deal with
inter-signal variability in a principled and automatic way. Our approach is a
generalized expectation maximization hyperparameter point estimation procedure
with variational Bayes at the level of individual time series that learns an
single interpretable representation of the overall data generating process. | Source: | arXiv, 1305.3640 | Services: | Forum | Review | PDF | Favorites |
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