Evaluating DNA Function Understanding in Genomic Language Models Using Evolutionarily Implausible Sequences.

Abstract excerpt

Genomic language models (gLMs) hold promise for generating novel, functional DNA sequences for synthetic biology. A critical challenge is determining whether gLMs understand sequence function or merely memorize training patterns derived from natural genomes. We introduce Nullsettes, an evaluation framework that measures how well models predict in silico loss-of-function (LOF) mutations in synthetic expression...

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