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F1000Res. 2016 Jun 28;5:1531. doi: 10.12688/f1000research.9054.1. eCollection 2016.

Robust de novo pathway enrichment with KeyPathwayMiner 5.

Author information

1
Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.
2
Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark; Institute of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark; Max Planck Institute for Informatics, 66123 Saarbrucken, Germany.
3
Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.
4
Integrated Research Institute (IRI) for the Life Sciences and Department of Biology, Humboldt-Universitat zu Berlin, 10099 Berlin, Germany.
5
Institute of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark; Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, 5000 Odense, Denmark.
6
Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark.
7
Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark; Department of Oncology, Odense University Hospital, 5000 Odense, Denmark.
8
Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Max Planck Institute for Informatics, 66123 Saarbrucken, Germany.

Abstract

Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that are enriched for differentially active entities from a series of molecular profiles encoded as binary indicator matrices. Since interaction networks constantly evolve, an important question is how robust the extracted results are when the network is modified. We enable users to study this effect through several network perturbation techniques and over a range of perturbation degrees. In addition, users may now provide a gold-standard set to determine how enriched extracted pathways are with relevant genes compared to randomized versions of the original network.

KEYWORDS:

Pathway enrichment; algorithms; data integration; network analysis; systems biology

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