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

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 BoehmJake Jaffe, Jill Mesirov, Nir Hacohen, Aviv Regev, Kasper Lage

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)543-546
Number of pages4
JournalNature Methods
Volume15
DOIs
Publication statusPublished - Jul 2018
Externally publishedYes

Keywords

  • functional genomics
  • genetics research
  • network topology
  • systems biology

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