A DNA Likelihood Is Not a Functional Assay: Genomic Foundation Models in 2026
A genomic model result is credible only when its tokenisation, strand rule, biological target, evaluation split, and artifact terms are explicit.
Updated most weeks/No. 72/Genomics · Proteins · DiscoveryEst. 2024
An applied-AI practice for sequence biology.
Pragmatic advisory and empirical analysis for machine learning in genomics, proteins, and drug discovery — what holds up on a held-out test set, what doesn't, and what it means if you build with it. The writing here is the practice thinking in the open.
Lead articleSep 1, 2026 · 27 min read
An RNA Sequence Is Not a Molecular State: RNA Foundation Models in 2026
RNA models share an alphabet, not an estimand: choose them by molecular object, output, information inputs, evidence class, and artifact contract.
#rna-foundation-models#computational-biologyRead →
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The beats we cover
All topics →01
Genomics & DNA models
Sequence models, variant-effect prediction, and the work to read non-coding DNA.
25 posts→
02
Proteins & structure
Single-sequence folding, protein language models, and structure without alignment.
6 posts→
03
Drug discovery
Contrastive screening, protein–ligand interaction, and design that survives the wet lab.
5 posts→
04
Interpretability & evaluation
What models encode, where they fail, and how evaluation and oversight hold up.
2 posts→
The practice
The reading is public. The advisory is where it's applied.
rewire.it works with biotech, pharma, and research teams deciding whether and how to adopt a given AI-in-bio method — feasibility reviews, evaluation design, and held-out benchmarking. Independent, candid about where machine learning helps and where it doesn't.