Fig. 1
From: Transforming L1000 profiles to RNA-seq-like profiles with deep learning
Model architecture. G and F are generators, DY and DX are discriminators, and E is a fully connected neural network model for RNA-seq profile extrapolation from landmark genes to the full genome. The pipeline inputs are measured expression of 978 landmark genes from L1000 profiles. The CycleGAN model in the pipeline predicts RNA-seq-like profiles for given L1000 profiles. The extrapolation model inputs are 978-dimensional vectors of predicted RNA-seq-like profiles. The model then predicts 23,614-dimensional vectors of RNA-seq-like profiles