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Nat Methods. 2018 Jul;15(7):543-546. doi: 10.1038/s41592-018-0039-6. Epub 2018 Jun 18.

GeNets: a unified web platform for network-based genomic analyses.

Author information

1
Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
2
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
3
Department of Electrical Engineering & Computer Science, MIT, Cambridge, MA, USA.
4
Johns Hopkins School of Medicine, Baltimore, MD, USA.
5
Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
6
Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.
7
Department of Statistics, Harvard University, Cambridge, MA, USA.
8
Department of Medicine, University of California, San Diego, San Diego, CA, USA.
9
Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA.
10
Department of Surgery, Harvard Medical School, Boston, MA, USA.
11
Howard Hughes Medical Institute, Department of Biology, MIT, Cambridge, MA, USA.
12
Department of Surgery, Massachusetts General Hospital, Boston, MA, USA. lage.kasper@mgh.harvard.edu.
13
Broad Institute of MIT and Harvard, Cambridge, MA, USA. lage.kasper@mgh.harvard.edu.
14
Department of Surgery, Harvard Medical School, Boston, MA, USA. lage.kasper@mgh.harvard.edu.
15
Institute for Biological Psychiatry, Mental Health Center Sct. Hans, University of Copenhagen, Roskilde, Denmark. lage.kasper@mgh.harvard.edu.

Abstract

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.

PMID:
29915188
PMCID:
PMC6450090
DOI:
10.1038/s41592-018-0039-6
[Indexed for MEDLINE]
Free PMC Article

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