ArticleFrontiers in neuroinformatics2017
Unsupervised Idealization of Ion Channel Recordings by Minimum Description Length: Application to Human PIEZO1-Channels.
Article in Frontiers in neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 25 citations in OpenAlex.
- Continuous, Low Latency Estimation of the Size and Shape of Single Proteins from Real-Time Nanopore Data.Analytical chemistry · 2026Article
- Molecular dynamics guided identification of a brighter variant of superfolder Green Fluorescent Protein with increased photobleaching resistance.Communications chemistry · 2025Article
- Mechanistic and structural studies reveal NRAP-1-dependent coincident activation of NMDARs.Cell reports · 2024Article
- Machine Learning Methods for the Analysis of the Patch-Clamp Signals.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Model-free idealization: Adaptive integrated approach for idealization of ion-channel currents.Biophysical journal · 2023Article
- 2D-dwell-time analysis with simulations of ion-channel gating using high-performance computing.Biophysical journal · 2023Article
- DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks.PloS one · 2022Article
- A Guide to Signal Processing Algorithms for Nanopore Sensors.ACS sensors · 2021Article
- Analysis of patchclamp recordings: model-free multiscale methods and software.European biophysics journal : EBJ · 2021Article
- CellSpecks: A Software for Automated Detection and Analysis of Calcium Channels in Live Cells.Biophysical journal · 2018Article
- Electrophysiological measurement of ion channels on plasma/organelle membranes using an on-chip lipid bilayer system.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Researchers can investigate the mechanistic and molecular basis of many physiological phenomena in cells by analyzing the fundamental properties of single ion channels. These analyses entail recording single channel currents and measuring current amplitudes and transition rates between conductance states. Since most electrophysiological recordings contain noise, the data analysis can proceed by idealizing the recordings to isolate the true currents from the noise. This de-noising can be accomplished with threshold crossing algorithms and Hidden Markov Models, but such procedures generally depend on inputs and supervision by the user, thus requiring some prior knowledge of underlying processes. Channels with unknown gating and/or functional sub-states and the presence in the recording of currents from uncorrelated background channels present substantial challenges to such analyses. Here we describe and characterize an idealization algorithm based on Rissanen's Minimum Description Length (MDL) Principle. This method uses minimal assumptions and idealizes ion channel recordings without requiring a detailed user input or
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Registered trials
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