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Non-Coding RNA 2017, 3(2), 20; doi:10.3390/ncrna3020020

Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach

Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, c/o Dipartimento di Matematica e Informatica, Viale A. Doria 6, 95125 Catania, Italy
Author to whom correspondence should be addressed.
Received: 15 January 2017 / Revised: 28 March 2017 / Accepted: 10 April 2017 / Published: 19 April 2017
(This article belongs to the Special Issue Bioinformatics Softwares and Databases for Non-Coding RNA Research)
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In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with expression data taken from The Cancer Genome Atlas (TCGA). Our system extends pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The framework enables the extraction, visualization, and analysis of statistically significant disease-specific subpathways through an easy to use web interface. Our analysis shows that the methodology is able to fill the gap in current techniques, allowing a more comprehensive analysis of the phenomena underlying disease states. View Full-Text
Keywords: pathway perturbation; subpathway analysis; microRNAs; drugs; functional analysis. pathway perturbation; subpathway analysis; microRNAs; drugs; functional analysis.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Alaimo, S.; Marceca, G.P.; Ferro, A.; Pulvirenti, A. Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach. Non-Coding RNA 2017, 3, 20.

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