Benchmarking interpretability of deep learning for predictive genomics: Recall, precision, and variability of feature attribution.

Abstract excerpt

Deep neural networks can model the nonlinear architecture of polygenic traits, yet the reliability of attribution methods to identify the genetic variants driving model predictions remains uncertain. We introduce a benchmarking framework that quantifies three aspects of interpretability: attribution recall, attribution precision, and stability, and apply it to deep learning models trained on UK Biobank genotypes...

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