|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Li, T, Kim, A, Rosenbluh, J, Horn, H, Greenfeld, L, An, D, Zimmer, A, Liberzon, A, Bistline, J, Natoli, T, Li, Y, Tsherniak, A, Narayan, R, Subramanian, A, Liefeld, T, Wong, B, Thompson, D, Calvo, S, Carr, S, Boehm, J, Jaffe, J, Mesirov, J, Hacohen, N, Regev, A, Lage, K|
|Date Published||2018 Jun 18|
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.
|Alternate Journal||Nat. Methods|