AttnLRP explanations of DNABERT-2 reliably capture known biological patterns in genomic sequences, showing that transformer-based genome language models can yield biologically meaningful insights comparable to CNNs.
Nature Reviews Genetics20(7), 389–403 (2019)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Evaluating Post-hoc Explanations of the Transformer-based Genome Language Model DNABERT-2
AttnLRP explanations of DNABERT-2 reliably capture known biological patterns in genomic sequences, showing that transformer-based genome language models can yield biologically meaningful insights comparable to CNNs.