Publication: Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study, accepted to Genome Biology This repository reproduces results from the EIG publication. To use EIG with your own deep learning model, please refer to the repository (https://bitbucket.org/biociphers/eig) which contains instructions to install the EIG package and example usage. Abstract Despite the success and fast adaptation of deep learning models in biomedical domains, their lack of interpretability remains an issue. Here we introduce Enhanced Integrated Gradients (EIG), a method to identify significant features associated with a specific prediction task. Using RNA splicing prediction as well as digit classification as case studies, we demonstrate that EIG improves upon the original Integrated Gradients method and produces sets of informative features. We then apply EIG to identify A1CF as a key regulator of liver specific alternative splicing, supporting this finding with subsequent analysis of relevant A1CF functional (RNA-seq) and binding data (PAR-CLIP).