Learning and interpreting the gene regulatory grammar in a deep learning framework
Fig 3
ResNet trained on simulated regulatory sequences against 8-mer shuffled negatives accurately models the regulatory grammar.
(a) The performance of five different ResNet models trained on simulated regulatory sequences against different k-mer shuffled negatives at predicting the regulatory class of the simulated regulatory sequences vs. TFs-shuffled negatives test dataset. (b) Actual labels of simulated regulatory grammar of the TFBS overlaid on t-SNE visualization of TFBS saliency values across neurons. (c) The sensitivity of predicted labels in (b) of the ResNet model trained on the simulated regulatory sequences against 8-mer shuffled negatives.