Framingham and TCGA were pivotal without AI; models in the loop could boost data generation
anshulkundaje · x · 2026-09-21
A discussion among genomics researchers. viriditax argues that pivotal data generation efforts like the Framingham Heart Study and TCGA were launched without a specific mechanistic model in mind, functioning more as "north stars"—end goals that forced the building of a useful technology stack, and which couldn't realistically be redone today. anhulkundaje (Stanford epigenomics professor) agrees, adding that these projects predate generative AI, and small tweaks with models in the loop across phases of data generation would have substantially increased bang for buck.
Related event: Scholars debate how science should generate data in the AI era(3 posts)→
More from Research
- Cell essay lays out how to define world models for biomedicine — marinkazitnik · 2026-09-21
- Ling 3.0 Tiny vs Gemma 26B-A4B: 3x Smaller VRAM, But Accuracy Halved — autonoma_2042 · 2026-09-21
- The real bottleneck is memory and compression, not context: dev argues compaction is a crutch — JoelMahon · 2026-09-21
- Nature Health Paper Proposes an 'Epidemiology of AI,' Arguing AI Is Now a Determinant of Health — EricTopol · 2026-09-21
- AI slop rejected papers will be endlessly resubmitted, warns researcher on record submission volume — menhguin · 2026-09-21
- Should LLMs be first-pass reviewers for every scientific paper? Researchers say yes — anshulkundaje · 2026-09-21