Matt Ploenzke

This paper introduces Global Importance Analysis (GIA), a method to quantify the effect size of genomic features in deep neural networks, demonstrating its application in predicting RNA-protein interactions.

This study shows that using exponential activations in the first layer of CNNs improves the interpretability and robustness of genomic sequence motif representations in both synthetic and real DNA data.

This paper argues for global importance analysis to quantify population-level importance of genomic features in deep neural networks, beyond traditional attribution methods.

This paper introduces a method to directly learn interpretable genomic sequence motifs through weight constraints and regularization, enabling the use of position weight matrices and information gain matrices.