Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model

Abstract In recent studies, miRNAs have been found to be extremely influential in many of the essential biological processes. They exhibit a self-regulatory mechanism through which they act as positive/negative regulators of expression of genes and other miRNAs. This has direct implications in the r...

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Autores principales:	Joseph J. Nalluri, Pratip Rana, Debmalya Barh, Vasco Azevedo, Thang N. Dinh, Vladimir Vladimirov, Preetam Ghosh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1c30583c69654ea8bd2054ad51a8e72f
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spelling	oai:doaj.org-article:1c30583c69654ea8bd2054ad51a8e72f2021-12-02T15:05:44ZDetermining causal miRNAs and their signaling cascade in diseases using an influence diffusion model10.1038/s41598-017-08125-42045-2322https://doaj.org/article/1c30583c69654ea8bd2054ad51a8e72f2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08125-4https://doaj.org/toc/2045-2322Abstract In recent studies, miRNAs have been found to be extremely influential in many of the essential biological processes. They exhibit a self-regulatory mechanism through which they act as positive/negative regulators of expression of genes and other miRNAs. This has direct implications in the regulation of various pathophysiological conditions, signaling pathways and different types of cancers. Studying miRNA-disease associations has been an extensive area of research; however deciphering miRNA-miRNA network regulatory patterns in several diseases remains a challenge. In this study, we use information diffusion theory to quantify the influence diffusion in a miRNA-miRNA regulation network across multiple disease categories. Our proposed methodology determines the critical disease specific miRNAs which play a causal role in their signaling cascade and hence may regulate disease progression. We extensively validate our framework using existing computational tools from the literature. Furthermore, we implement our framework on a comprehensive miRNA expression data set for alcohol dependence and identify the causal miRNAs for alcohol-dependency in patients which were validated by the phase-shift in their expression scores towards the early stages of the disease. Finally, our computational framework for identifying causal miRNAs implicated in diseases is available as a free online tool for the greater scientific community.Joseph J. NalluriPratip RanaDebmalya BarhVasco AzevedoThang N. DinhVladimir VladimirovPreetam GhoshNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-14 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Joseph J. Nalluri Pratip Rana Debmalya Barh Vasco Azevedo Thang N. Dinh Vladimir Vladimirov Preetam Ghosh Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model
description	Abstract In recent studies, miRNAs have been found to be extremely influential in many of the essential biological processes. They exhibit a self-regulatory mechanism through which they act as positive/negative regulators of expression of genes and other miRNAs. This has direct implications in the regulation of various pathophysiological conditions, signaling pathways and different types of cancers. Studying miRNA-disease associations has been an extensive area of research; however deciphering miRNA-miRNA network regulatory patterns in several diseases remains a challenge. In this study, we use information diffusion theory to quantify the influence diffusion in a miRNA-miRNA regulation network across multiple disease categories. Our proposed methodology determines the critical disease specific miRNAs which play a causal role in their signaling cascade and hence may regulate disease progression. We extensively validate our framework using existing computational tools from the literature. Furthermore, we implement our framework on a comprehensive miRNA expression data set for alcohol dependence and identify the causal miRNAs for alcohol-dependency in patients which were validated by the phase-shift in their expression scores towards the early stages of the disease. Finally, our computational framework for identifying causal miRNAs implicated in diseases is available as a free online tool for the greater scientific community.
format	article
author	Joseph J. Nalluri Pratip Rana Debmalya Barh Vasco Azevedo Thang N. Dinh Vladimir Vladimirov Preetam Ghosh
author_facet	Joseph J. Nalluri Pratip Rana Debmalya Barh Vasco Azevedo Thang N. Dinh Vladimir Vladimirov Preetam Ghosh
author_sort	Joseph J. Nalluri
title	Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model
title_short	Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model
title_full	Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model
title_fullStr	Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model
title_full_unstemmed	Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model
title_sort	determining causal mirnas and their signaling cascade in diseases using an influence diffusion model
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/1c30583c69654ea8bd2054ad51a8e72f
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Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model

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