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Computation 2017, 5(2), 20; doi:10.3390/computation5020020

Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection

1
Department of Computer Engineering and Informatics, University of Patras, Patras 26500, Greece
2
Centre for Cancer Biomarkers CCBIO and Computational Biology Unit, Department of Informatics, University of Bergen, Bergen 5020, Norway
3
Department of Informatics, Ionian University Corfu, Corfu 49100, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Demos T. Tsahalis
Received: 31 December 2016 / Revised: 19 March 2017 / Accepted: 29 March 2017 / Published: 3 April 2017
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Abstract

It has already been established by the systems-level approaches that the future of predictive disease biomarkers will not be sketched by plain lists of genes or proteins or other biological entities but rather integrated entities that consider all underlying component relationships. Towards this orientation, early pathway-based approaches coupled expression data with whole pathway interaction topologies but it was the recent approaches that zoomed into subpathways (local areas of the entire biological pathway) that provided more targeted and context-specific candidate disease biomarkers. Here, we explore the application potential of PerSubs, a graph-based algorithm which identifies differentially activated disease-specific subpathways. PerSubs is applicable both for microarray and RNA-Seq data and utilizes the Kyoto Encyclopedia of Genes and Genomes (KEGG) database as reference for biological pathways. PerSubs operates in two stages: first, identifies differentially expressed genes (or uses any list of disease-related genes) and in second stage, treating each gene of the list as start point, it scans the pathway topology around to build meaningful subpathway topologies. Here, we apply PerSubs to investigate which pathways are perturbed towards mouse lung regeneration following H1N1 influenza infection. View Full-Text
Keywords: lung regeneration; systems biology; computation on networks and graphs lung regeneration; systems biology; computation on networks and graphs
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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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MDPI and ACS Style

Vrahatis, A.G.; Dimitrakopoulou, K.; Kanavos, A.; Sioutas, S.; Tsakalidis, A. Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection. Computation 2017, 5, 20.

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