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Maxam-Gilbert Sequencing

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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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Published on: January 30, 2018

A new method for inferring hidden markov models from noisy time sequences.

David Kelly1, Mark Dillingham, Andrew Hudson

  • 1School of Mathematics, University of Bristol, Bristol, United Kingdom. dk3531@bristol.ac.uk

Plos One
|January 17, 2012
PubMed
Summary

This study introduces a novel method for analyzing noisy time-series data to infer hidden Markov models without pre-defined structures, enabling the discovery of complex system dynamics and degenerate states.

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Area of Science:

  • Complex Systems Analysis
  • Statistical Physics
  • Biophysics

Background:

  • Hidden Markov models (HMMs) are widely used for time-series analysis.
  • Traditional HMM inference often requires prior assumptions about model architecture.
  • Inferring degenerate states and hidden dynamics remains challenging.

Purpose of the Study:

  • To develop a novel method for inferring HMMs from noisy time sequences.
  • To enable the detection of degenerate states without assuming a model architecture.
  • To apply the method to continuous data exhibiting discrete clustering and transitions.

Main Methods:

  • Utilizes statistical prediction techniques based on Crutchfield et al.
  • Generates causal state models equivalent to HMMs.
  • Applies algorithms to continuous data clustering around discrete values with multiple transitions.

Main Results:

  • Successfully infers underlying models from simulated data under high noise and sparsity.
  • Demonstrates the ability to detect degenerate states.
  • Accurately models experimental Fluorescence Resonance Energy Transfer (FRET) data for Holliday Junction dynamics, yielding comparable transition rates to existing methods.

Conclusions:

  • The new method effectively infers HMMs without architectural assumptions, revealing hidden states.
  • It is applicable to diverse continuous time-series data, including biological measurements.
  • Offers a robust alternative for analyzing complex dynamic systems.