ArticleEuropean biophysics journal : EBJ2015
Hidden Markov analysis of improved bandwidth mechanosensitive ion channel data.
Article in European biophysics journal : EBJ, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Activating mechanosensitive channels embedded in droplet interface bilayers using membrane asymmetry.Chemical science · 2021Article
- Moving average filtering with deconvolution (MAD) for hidden Markov model with filtering and correlated noise.European biophysics journal : EBJ · 2019Article
- Biophysics of Mechanotransduction.European biophysics journal : EBJ · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The gating behaviour of a single ion channel can be described by hidden Markov models (HMMs), forming the basis for statistical analysis of patch clamp data. Extensive improved bandwidth (25 kHz, 50 kHz) data from the mechanosensitive channel of large conductance in Escherichia coli were analysed using HMMs, and HMMs with a moving average adjustment for filtering. The aim was to determine the number of levels, and mean current, mean dwell time and proportion of time at each level. Parameter estimates for HMMs with a moving average adjustment for low-pass filtering were obtained using an expectation-maximisation algorithm that depends on a generalisation of Baum's forward-backward algorithm. This results in a simpler algorithm than those based on meta-states and a much smaller parameter space; hence, the computational load is substantially reduced. In addition, this algorithm maximises the actual log-likelihood rather than that for a related meta-state process. Comprehensive data analyses and comparisons across all our data sets have consistently shown five subconducting levels in addition to the fully open and closed levels for this channel.
Indexed as
Identifiers
26233758What OpenQuestion holds
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.