What DNA Foundation Models Learn Beyond Sequence Composition
Do genomic foundation models (FMs) provide predictive information beyond simple sequence composition?
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Key points
- We evaluate frozen representations from five DNA FMs against normalized k-mer frequencies across 57 genomic classification datasets and two exploratory gene-expression regression tasks.
- To test incremental predictive utility directly, we compare global 6-mer features with their concatenation with each FM embedding under a matched Random Forest protocol.
- Exact 6-mer-preserving shuffles further reveal task-relevant sequence-order sensitivity, most pronounced for NTv3, although order sensitivity alone does not guarantee predictive improvements.
- Our results establish compositional features as essential controls and offer complementary diagnostics for evaluating the added value of frozen genomic representations, without making claims about fine-tuning capacity.
Sources (1)
- [1]What DNA Foundation Models Learn Beyond Sequence CompositionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 03:51 PM
Do genomic foundation models (FMs) provide predictive information beyond simple sequence composition?
We evaluate frozen representations from five DNA FMs against normalized k-mer frequencies across 57 genomic classification datasets and two exploratory gene-expression regression tasks.
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