Evidence map›Paper›PMID 31028435›Full record

ArticleEuropean biophysics journal : EBJ2019

Moving average filtering with deconvolution (MAD) for hidden Markov model with filtering and correlated noise.

Ibrahim M Almanjahie, Ramzan Nazim Khan, Robin K Milne, Takeshi Nomura, Boris Martinac

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Article in European biophysics journal : EBJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Ibrahim M AlmanjahieDepartment of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia.ORCID http://orcid.org/0000-0002-4651-3210
Ramzan Nazim KhanDepartment of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia. nazim.khan@uwa.edu.au.ORCID http://orcid.org/0000-0003-3349-5006
Robin K MilneDepartment of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia.ORCID http://orcid.org/0000-0002-1098-2672
Takeshi NomuraDepartment of Rehabilitation, Kyushu Nutrition Welfare University, Kitakyushu, 800-029, Japan.ORCID http://orcid.org/0000-0001-8955-3426
Boris MartinacMechanosensory Biophysics Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia.ORCID http://orcid.org/0000-0001-8422-7082

Funding

National Health and Medical Research Council APP1047980
6 · The paper itself

Abstract

Ion channel data recorded using the patch clamp technique are low-pass filtered to remove high-frequency noise. Almanjahie et al. (Eur Biophys J 44:545-556, 2015) based statistical analysis of such data on a hidden Markov model (HMM) with a moving average adjustment for the filter but without correlated noise, and used the EM algorithm for parameter estimation. In this paper, we extend their model to include correlated noise, using signal processing methods and deconvolution to pre-whiten the noise. The resulting data can be modelled as a standard HMM and parameter estimates are again obtained using the EM algorithm. We evaluate this approach using simulated data and also apply it to real data obtained from the mechanosensitive channel of large conductance (MscL) in Escherichia coli. Estimates of mean conductances are comparable to literature values. The key advantages of this method are that it is much simpler and computationally considerably more efficient than currently used HMM methods that include filtering and correlated noise.

Indexed as

Data AnalysisMarkov ChainsAlgorithmsComputational BiologyEscherichia coli ProteinsIon ChannelsEscherichia coli ProteinsIon ChannelsMscL protein, E coliCorrelated noiseDeconvolutionEM algorithmFilter approximationHidden Markov modelsLevel-dependent noiseMscLParameter estimationPatch clamp

Identifiers

PMID31028435

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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.