{Reference Type}: Journal Article
{Title}: Correcting gradient-based interpretations of deep neural networks for genomics.
{Author}: Majdandzic A;Rajesh C;Koo PK;
{Journal}: Genome Biol
{Volume}: 24
{Issue}: 1
{Year}: 2023 05 9
暂无{DOI}: 10.1186/s13059-023-02956-3
{Abstract}: Post hoc attribution methods can provide insights into the learned patterns from deep neural networks (DNNs) trained on high-throughput functional genomics data. However, in practice, their resultant attribution maps can be challenging to interpret due to spurious importance scores for seemingly arbitrary nucleotides. Here, we identify a previously overlooked attribution noise source that arises from how DNNs handle one-hot encoded DNA. We demonstrate this noise is pervasive across various genomic DNNs and introduce a statistical correction that effectively reduces it, leading to more reliable attribution maps. Our approach represents a promising step towards gaining meaningful insights from DNNs in regulatory genomics.