GeNets: A unified web platform for network-based analyses of genomic data

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a par...

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Bibliographic Details
Published inNature methods Vol. 15; no. 7; pp. 543 - 546
Main Authors Li, Taibo, Kim, April, Rosenbluh, Joseph, Horn, Heiko, Greenfeld, Liraz, An, David, Zimmer, Andrew, Liberzon, Arthur, Bistline, Jon, Natoli, Ted, Li, Yang, Tsherniak, Aviad, Narayan, Rajiv, Subramanian, Aravind, Liefeld, Ted, Wong, Bang, Thompson, Dawn, Calvo, Sarah, Carr, Steve, Boehm, Jesse, Jaffe, Jake, Mesirov, Jill, Hacohen, Nir, Regev, Aviv, Lage, Kasper
Format Journal Article
LanguageEnglish
Published 18.06.2018
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Summary:Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.
Bibliography:Developed the GeNets Platform: TL, AK, HH, LG, DA, AZ, JB, BW, AR, KL.
AUTHOR CONTRIBUTIONS (ordered based on overall author list)
Analyzed data and performed experiments: TL, AK, JR, HH, LG, DA, AZ, AL, JB, TN, YL, AT, RN, AS, TL, BW, DT, SC, SC, JB, JJ, JM, NH, AR, KL. Wrote paper: TL and KL with input from all authors. Initiated, designed and led the project: KL
ISSN:1548-7091
1548-7105
DOI:10.1038/s41592-018-0039-6