Review argues perturbation data is key to fixing seq-to-func genomics models
anshulkundaje · x · 2026-07-28
The thread says seq-to-func models perform well on held-out genomic regions, but their generalization breaks down across genetic variation and cellular contexts. The published review argues this inconsistency is a central problem and points to a path forward: use targeted perturbation experiments to feed iterative model refinement.
It also says the review covers the broader landscape of regulatory genomics modeling, including:
- loss landscapes
- out-of-distribution generalization
- model interpretability
- active and continual learning with perturbation data
The core message is that better experimental feedback loops, not just larger models, are needed to fix generalization in this area.
Related event: Nature Genetics Review Tackles AI Generalization in Regulatory Genomics(3 posts)→
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