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

By Taibo Li, April Kim, Joseph Rosenbluh, Heiko Horn, Liraz Greenfeld, David An, Andrew Zimmer, Arthur Liberzon, Jon Bistline, Ted Natoli, Yang Li, Aviad Tsherniak, Rajiv Narayan, Aravind Subramanian, Ted Liefeld, Bang Wong, Dawn Thompson, Sarah Calvo, Steve Carr, Jesse Boehm, Jake Jaffe, Jill Mesirov, Nir Hacohen, Aviv Regev, K Lage

Posted 19 Apr 2018
bioRxiv DOI: 10.1101/196303 (published 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.

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