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Novel EM based ML Kalman estimation framework for superresolution of stochastic three-states microtubule signal

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dc.contributor.authorMenon, Vineetha
dc.contributor.authorYarahmadian, Shantia
dc.contributor.authorRezania, Vahid
dc.date.accessioned2020-11-24
dc.date.accessioned2022-05-31T01:16:33Z
dc.date.available2022-05-31T01:16:33Z
dc.date.issued2018
dc.description.abstractThis work aims to address limited data availability and data/observation loss incurred due to non-uniform sampling of biological signals such as MTs. For this purpose, statistical modelling of stochastic MT signals using EM based ML driven Kalman estimation (MLK) is considered as a fundamental framework for prediction of missing MT observations. It was experimentally validated that the proposed superresolution methods provided superior overall performance, better MT signal estimation using fewer samples, high SNR, low errors, and better MT parameter estimation than other methods.
dc.format.extent1.90MB
dc.format.mimetypePDF
dc.identifier.citationVineetha Menon, Shantia Yarahmadian, Vahid Rezania. Novel EM based ML Kalman estimation framework for superresolution of stochastic three-states microtubule signal. BMC Syst Biol. 2018; 12(Suppl 6): 112. Published online 2018 Nov 22. doi:10.1186/s12918-018-0631-5
dc.identifier.doihttps://doi.org/10.1186/s12918-018-0631-5
dc.identifier.urihttps://hdl.handle.net/20.500.14078/2069
dc.languageEnglish
dc.language.isoen
dc.rightsAttribution (CC BY)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectsuperresolution
dc.subjectKalman filtering
dc.subjectexpectation maximization
dc.subjectwavelets
dc.subjectprincipal component analysis
dc.subjectmutual information
dc.subjectmissing data
dc.titleNovel EM based ML Kalman estimation framework for superresolution of stochastic three-states microtubule signalen
dc.typeArticle
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