RT Journal Article
SR Electronic
T1 Robust Neural Networks are More Interpretable for Genomics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 657437
DO 10.1101/657437
A1 Koo, Peter K.
A1 Qian, Sharon
A1 Kaplun, Gal
A1 Volf, Verena
A1 Kalimeris, Dimitris
YR 2019
UL http://biorxiv.org/content/early/2019/06/03/657437.abstract
AB Deep neural networks (DNNs) have been applied to a variety of regulatory genomics tasks. For interpretability, attribution methods are employed to provide importance scores for each nucleotide in a given sequence. However, even with state-of-the-art DNNs, there is no guarantee that these methods can recover interpretable, biological representations. Here we perform systematic experiments on synthetic genomic data to raise awareness of this issue. We find that deeper networks have better generalization performance, but attribution methods recover less interpretable representations. Then, we show training methods promoting robustness – including regularization, injecting random noise into the data, and adversarial training – significantly improve interpretability of DNNs, especially for smaller datasets.