AlphaGenome is fine-tuned on RNA-seq modalities beyond coverage tracks
anshulkundaje · x · 2026-07-29
A genomicsxai blog post from Miquel Anglada-Girotto describes a framework for preprocessing, loading, and fine-tuning AlphaGenome on multiple RNA-seq modalities, including gene expression, splice-site usage, and splice junctions.
The post explains the motivation: adapting AlphaGenome to RNA-seq samples that were not part of pretraining, and evaluating whether the model can learn sample-specific multimodal transcriptomic patterns on held-out genomic intervals. As a case study, it uses an SF3B1 K700E cancer-driver sample and a wild-type control, because the mutation is known to alter splicing and promote cryptic splice sites.
Related event: Team Fine-Tunes AlphaGenome for RNA-seq Data Adaptation(4 posts)→
More from Research
- MIT's Neil Thompson: AI comes for tasks before jobs, and fixed costs favor big firms — bigdata · 2026-10-16
- UMD's PAIR framework bridges perception and action in VLA models via bridge tokens — Kaixi Feng · 2026-10-16
- CI-MSE offline metric hits -0.87 Spearman correlation with real robot task success vs weak raw MSE — chris_j_paxton · 2026-10-16
- Colossal Biosciences uses AI to decode genetic switches behind the extinct dodo — castrotech · 2026-10-16
- Colossal Biosciences uses AI to identify gene switches in dodo de-extinction push — castrotech · 2026-10-16
- Cheng Lou open-sources Handwired: hand-written weights turn code into training-free neural nets — dotey · 2026-10-16