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

• Published in: Nature 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
• Language: English
• Published: 18-06-2018
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/s41592-018-0039-6

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.