Evaluating Post-hoc Explanations of the Transformer-based Genome Language Model DNABERT-2

ArXi:2604.21690v1 Announce Type: new Explaining deep neural network predictions on genome sequences enables biological insight and hypothesis generation-often of greater interest than predictive performance alone. While explanations of convolutional neural networks (CNNs) have been shown to capture relevant patterns in genome sequences, it is unclear whether this transfers to expressive Transformer-based genome language models (gLMs