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 language | English |
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Pages (from-to) | 543-546 |
Number of pages | 4 |
Journal | Nature Methods |
Volume | 15 |
DOIs | |
Publication status | Published - Jul 2018 |
Externally published | Yes |
Keywords
- functional genomics
- genetics research
- network topology
- systems biology