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Fig. 2 | Genome Biology

Fig. 2

From: Correcting gradient-based interpretations of deep neural networks for genomics

Fig. 2

Visualizing 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 comparison

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