# J-solo-claude-science

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Jerome Irudayanathan](https://cerebralvalley.ai/u/madd-ebb)
- **GitHub:** https://github.com/jerome-f/Claude_Life_Sciences_Hackathon_Project.git; https://jerome-f.github.io/Claude_Life_Sciences_Hackathon_Project/
- **Demo video:** https://youtu.be/fY6m1YjTCGA
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/295

What I built and investigated. GWAS have catalogued tens of thousands of trait-associated loci, but naming the causal effector gene — usually acting through a non-coding regulatory variant far from the gene — remains unsolved at scale. The field's default evidence, cis-QTL colocalization, has a ceiling that is larger than commonly stated. I set out to (i) measure that gap rigorously and (ii) build a sequence-based line of evidence that adds what colocalization cannot: a directional enhancer→gene link available where colocalization is silent. Over the past week this ran end to end: I assembled a harmonized five-source, 10M-row multi-biobank credible-set substrate (All-of-Us, UKB-WGS, FinnGen R13, BBJ molecular-QTL, UKB metabolomics); harvested published colocalization results from UKB-PPP, FinnGen R13 and BBJ; derived 6.7M FDR-controlled signed enhancer→gene pairs from the DBNascent nascent-RNA atlas (validated to Pearson = 1.0 against published pairs, plus a clean-room mouse reimplementation); embedded ~1.2M regulatory windows through a frozen Nucleotide Transformer v2; and trained a family of signed-E2G models culminating in CATE, a convolution-attention two-tower encoder.

What I found. Three results. (1) The gap is real and large: on my own substrate only 1–2% of confident QTL signals share a causal lead with a GWAS signal despite ~90% being feasibly close, and on a 1,160-locus benchmark cis-eQTL colocalization names the curated effector at only 24.5% of loci (44.7% silent, 30.9% wrong — 75.6% unresolved), corroborated by three flagship resources. (2) A DBNascent-derived sign predicts causal direction (Spearman ρ = −0.224, p = 5×10⁻⁹; blood×K562 odds ratio 5.4) — weak but robust, and unique among E2G methods. (3) The model is complementary to distance: it recovers 39 effector genes distance misses (18 at coloc-silent loci, including GLP1R, MYC, SNCA, VDR), and a methodological finding — link AUROC plateaus at ~0.61 across gradient-boosted trees, a pooled MLP, and cross-attention — localizes the ceiling to pooling away sequence positions, motivating the per-token CATE model.

Why it matters. Effector-gene assignment is the bottleneck between a GWAS hit and a drug target: get the gene wrong and every downstream decision is wrong too. Quantifying exactly where the standard tool fails, and adding a sequence-only, directional signal available at the silent majority of loci, gives a more honest and more complete locus-to-gene map.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/295. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt