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NPJ Syst Biol Appl. 2017 Mar 3;3:6. doi: 10.1038/s41540-017-0007-2. eCollection 2017.

On the performance of de novo pathway enrichment.

Author information

1
Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
2
Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.
3
Department of Dermatology and Allergy, Technical University of Munich, Munich, Germany.
4
Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark.
5
Center for Bioinformatics, Saarland University Campus, Saarbrücken, Germany.
6
Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark.
7
Department of Oncology, Odense University Hospital, Odense, Denmark.
8
University of Greifswald, Institute for Mathematics and Computer Science, Greifswald, Germany.
9
Computational Systems Biology group, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.
10
Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.

Abstract

De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art.

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