Assessment of network module identification across complex diseases
In this DREAM challenge, 75 methods for the identification of disease-relevant modules from molecular networks are compared and validated with GWAS data. The authors provide practical guidelines for users and establish benchmarks for network analysis.
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
30 August 2019
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| In: |
Nature methods
Year: 2019, Jahrgang: 16, Heft: 9, Pages: 843-852 |
| ISSN: | 1548-7105 |
| DOI: | 10.1038/s41592-019-0509-5 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41592-019-0509-5 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41592-019-0509-5 
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| Verfasserangaben: | Sarvenaz Choobdar, Mehmet E. Ahsen, Jake Crawford, Mattia Tomasoni, Tao Fang, David Lamparter, Junyuan Lin, Benjamin Hescott, Xiaozhe Hu, Johnathan Mercer, Ted Natoli, Rajiv Narayan, The DREAM Module Identification Challenge Consortium, Aravind Subramanian, Jitao D. Zhang, Gustavo Stolovitzky, Zoltán Kutalik, Kasper Lage, Donna K. Slonim, Julio Saez-Rodriguez, Lenore J. Cowen, Sven Bergmann and Daniel Marbach |
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