ArticleBiophysical journal2002
Applying hidden Markov models to the analysis of single ion channel activity.
Article in Biophysical journal, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Nanoscale organization in the cell membrane dynamically modulates the biophysics of voltage-gated sodium channels.Nature communications · 2026Article
- Celebrating 50 Years of Single-Channel Recording with the Patch Clamp.The Journal of membrane biology · 2025Review
- Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models.bioRxiv : the preprint server for biology · 2025Article
- A deep learning approach to real-time Markov modeling of ion channel gating.Communications chemistry · 2024Article
- 2D-dwell-time analysis with simulations of ion-channel gating using high-performance computing.Biophysical journal · 2023Article
- Article
- Wavelet Denoising of High-Bandwidth Nanopore and Ion-Channel Signals.Nano letters · 2019Article
- Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints.The Journal of general physiology · 2018Article
- Estimating kinetic mechanisms with prior knowledge II: Behavioral constraints and numerical tests.The Journal of general physiology · 2018Article
- Quantum theory of mass potentials.PloS one · 2018Article
- Extended beta distributions open the access to fast gating in bilayer experiments-assigning the voltage-dependent gating to the selectivity filter.FEBS letters · 2017Article
- Enzyme-Mediated Directional Transport of a Small-Molecule Walker With Chemically Identical Feet.Journal of the American Chemical Society · 2017Article
- An Introduction to Infinite HMMs for Single-Molecule Data Analysis.Biophysical journal · 2017Review
- ICON: An Adaptation of Infinite HMMs for Time Traces with Drift.Biophysical journal · 2017Article
- Unsupervised Idealization of Ion Channel Recordings by Minimum Description Length: Application to Human PIEZO1-Channels.Frontiers in neuroinformatics · 2017Article
- Is realistic neuronal modeling realistic?Journal of neurophysiology · 2016Review
- Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction.Biophysical journal · 2016Article
- Improved resolution of single channel dwell times reveals mechanisms of binding, priming, and gating in muscle AChR.The Journal of general physiology · 2016Article
- How to resolve microsecond current fluctuations in single ion channels: the power of beta distributions.Channels (Austin, Tex.) · 2015Review
- Membrane current series monitoring: essential reduction of data points to finite number of stable parameters.Frontiers in computational neuroscience · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Hidden Markov models have recently been used to model single ion channel currents as recorded with the patch clamp technique from cell membranes. The estimation of hidden Markov models parameters using the forward-backward and Baum-Welch algorithms can be performed at signal to noise ratios that are too low for conventional single channel kinetic analysis; however, the application of these algorithms relies on the assumptions that the background noise be white and that the underlying state transitions occur at discrete times. To address these issues, we present an "H-noise" algorithm that accounts for correlated background noise and the randomness of sampling relative to transitions. We also discuss three issues that arise in the practical application of the algorithm in analyzing single channel data. First, we describe a digital inverse filter that removes the effects of the analog antialiasing filter and yields a sharp frequency roll-off. This enhances the performance while reducing the computational intensity of the algorithm. Second, the data may be contaminated with baseline drifts or deterministic interferences such as 60-Hz pickup. We propose an extension of previous results to consider baseline drift. Finally, we describe the extension of the algorithm to multiple data sets.
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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.