Interpretable Convolution Methods for Learning Genomic Sequence Motifs
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.
bioRxiv · Sep 8, 2018 · PreprintBioinformatics