Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/57573
Type of publication: Konferencijų tezės nerecenzuojamuose leidiniuose / Conference theses in non-peer-reviewed publications (T2)
Field of Science: Informatika / Informatics (N009)
Author(s): Užupytė, Rūta;Krilavičius, Tomas;Navickas, R;Šatrauskienė, A
Title: An exploratory enalysis of the relation between metabolic syndrome factors and microRNA data
Is part of: Data analysis methods for software systems : 8th international workshop, Druskininkai, Lithuania, December 1-3, 2016 : [abstracts book]. Vilnius : Vilnius university Institute of data science and digital technologies, 2016
Extent: p. 60-61
Date: 2016
ISBN: 9789986680611
Abstract: The metabolic syndrome (MetS) is associated with increased risk for cardiovascular disease. The detection and treatment of the underlying factors of the metabolic syndrome have a significant influence on the reduction of the cardiovascular disease. In this study, we analyze relations between MetS components and RNA molecules (microRNAs) regulating gene expression at the posttranscriptional level, in order to determine the predictive value of different microRNAs for subjects with metabolic syndrome. We apply correlation and linear regression to analyze the relationship between microRNAs and selected arterial markers. Logistic regression models were used to explore the statistical relationship between microRNAs and categorical variables. Results show that statistically significant linear relationship exists between arterial markers and several microRNAs, however, the observed relationship is very weak (<0.25). Since cardiovascular diseases are usually multifactorial diseases, caused by various mechanisms, it is more likely, that the combination of microRNAs will have stronger predictional or diagnostic power. Moreover, it is possible that more valuable results can be obtained by analyzing relations between microRNAs and binary variable determining the absence/existence of metabolic syndrome. Hence, we plan to use canonical correlation analysis to investigate linear combinations of microRNAs which have a maximum correlation with arterial markers
Internet: https://hdl.handle.net/20.500.12259/57573
Affiliation(s): Baltijos pažangių technologijų institutas
Baltijos pažangių technologijų institutas, Vilnius
Informatikos fakultetas
Taikomosios informatikos katedra
Vilniaus universitetas
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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