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Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study
2020
Genome Biology
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
doi:10.1186/s13059-020-02055-7
pmid:32560708
fatcat:5xymliqksbhkrlhfn7gwraffry