Fig. 2From: Correcting gradient-based interpretations of deep neural networks for genomicsVisualizing the gradient correction. Sequence logo of the uncorrected saliency map (top row), gradient angles at each position (second row), and corrected saliency map (third row) for a patch from representative test sequences. a, b CNN-deep-relu trained to make binary predictions on a synthetic data and b ChIP-seq data for ATF2 protein in GM12878. The sequence logo of ground truth is shown for CNN-deep-exp for a synthetic data. b An ensemble average saliency map is shown in lieu of ground truth (bottom row). c–e A similar plot is made for a c DeepSTARR model trained to predict enhancer activity via STARR-seq data, d Basset model trained to make binary predictions of chromatin accessibility sites via DNase-seq data, and e CNN model trained to predict base-resolution read-coverage values from ATAC-seq data in PC-3 cell line. c–e A colored box and a corresponding sequence logo of a known motif from JASPAR [20] (with a corresponding ID) or Ref. [21] are shown for comparisonBack to article page