Improving interpretability of deep learning models: splicing codes as a case study

Jha, A.; Aicher, J. K.; Singh, D.; Barash, Y.

doi:10.1101/700096

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bioRxiv · 10.1101/700096

Improving interpretability of deep learning models: splicing codes as a case study

Jha, A.·Aicher, J. K.·Singh, D.·Barash, Y.

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Abstract

Despite the success and fast adaptation of deep learning models in a wide range of fields, lack of interpretability remains an issue, especially in biomedical domains. A recent promising method to address this limitation is Integrated Gradients (IG), which identifies features associated with a prediction by traversing a linear path from a baseline to a sample. We extend IG with nonlinear paths, embedding in latent space, alternative baselines, and a framework to identify important features which make it suitable for interpretation of deep models for genomics.

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Jha, A., Aicher, J. K., Singh, D., Barash, Y.. 2019-07-14. Improving interpretability of deep learning models: splicing codes as a case study. https://doi.org/10.1101/700096

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