ArticlePloS one2012
A new method for inferring hidden markov models from noisy time sequences.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories.Nature communications · 2022Article
- Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes.Biophysical journal · 2021Article
- DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.eLife · 2020Article
- A Bayesian Nonparametric Approach to Single Molecule Förster Resonance Energy Transfer.The journal of physical chemistry. B · 2019Article
- Efficient use of single molecule time traces to resolve kinetic rates, models and uncertainties.The Journal of chemical physics · 2018Article
- Data Driven Models of Short-Term Synaptic Plasticity.Frontiers in computational neuroscience · 2018Article
- Extreme Quantum Advantage when Simulating Classical Systems with Long-Range Interaction.Scientific reports · 2017Article
- Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis.Chemical reviews · 2017Article
- Single-Molecule Analysis beyond Dwell Times: Demonstration and Assessment in and out of Equilibrium.Biophysical journal · 2016Article
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Authors and funding
4 authors.
Funding
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Abstract
We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al. and generates so called causal state models, equivalent in structure to hidden Markov models. The new method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods. The method differs markedly from previous Markov-model reconstructions in being able to uncover truly hidden states.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.