ArticleBiophysical journal2017
ICON: An Adaptation of Infinite HMMs for Time Traces with Drift.
Article in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
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- A cautious user's guide in applying HMMs to physical systems.The Journal of chemical physics · 2025Article
- Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting.Biophysical journal · 2025Article
- Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting.bioRxiv : the preprint server for biology · 2025Article
- Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models.bioRxiv : the preprint server for biology · 2025Article
- An accurate probabilistic step finder for time-series analysis.Biophysical journal · 2024Article
- Model-free idealization: Adaptive integrated approach for idealization of ion-channel currents.Biophysical journal · 2023Article
- An accurate probabilistic step finder for time-series analysis.bioRxiv : the preprint server for biology · 2023Article
- Unlicensed origin DNA melting by MCV and SV40 polyomavirus LT proteins is independent of ATP-dependent helicase activity.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Monte Carlo samplers for efficient network inference.PLoS computational biology · 2023Article
- Learning continuous potentials from smFRET.Biophysical journal · 2023Article
- Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap.iScience · 2022Article
- Modeling Non-additive Effects in Neighboring Chemically Identical Fluorophores.The journal of physical chemistry. B · 2022Article
- Article
- Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics.Nature computational science · 2022Article
- Extraction of rapid kinetics from smFRET measurements using integrative detectors.Cell reports. Physical science · 2021Article
- Residence time analysis of RNA polymerase transcription dynamics: A Bayesian sticky HMM approach.Biophysical journal · 2021Article
- Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes.Biophysical journal · 2021Article
- Direct Photon-by-Photon Analysis of Time-Resolved Pulsed Excitation Data using Bayesian Nonparametrics.Cell reports. Physical science · 2020Article
- The Maximum Caliber Variational Principle for Nonequilibria.Annual review of physical chemistry · 2020Review
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2 authors.
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Abstract
Bayesian nonparametric methods have recently transformed emerging areas within data science. One such promising method, the infinite hidden Markov model (iHMM), generalizes the HMM that itself has become a workhorse in single molecule data analysis. The iHMM goes beyond the HMM by self-consistently learning all parameters learned by the HMM in addition to learning the number of states without recourse to any model selection steps. Despite its generality, simple features (such as drift), common to single molecule time traces, result in an overinterpretation of drift and the introduction of artifact states. Here we present an adaptation of the iHMM that can treat data with drift originating from one or many traces (e.g., Förster resonance energy transfer). Our fully Bayesian method couples the iHMM to a continuous control process (drift) self-consistently learned while learning all other quantities determined by the iHMM (including state numbers). A key advantage of this method is that all traces-regardless of drift or states visited across traces-may now be treated on an equal footing, thereby eliminating user-dependent trace selection (based on drift levels), preprocessing to remove drift, and postprocessing model selection based on state number.
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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.