ArticleNature methods2026
A scalable approach to investigating sequence-to-function predictions from personal genomes.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Epigenetic conditioning improves sequence-based modeling of gene regulation across cell types and alleles.bioRxiv : the preprint server for biology · 2026Article
- Deep-learning prediction of gene expression from personal genomes.Genome biology · 2026Article
- Fine-tuning sequence to function deep learning models on large-scale proteomic data improves the accuracy of variant effect prediction.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Sequence-to-function (S2F) models can evaluate arbitrary DNA sequences, yet they struggle to fully capture inter-individual variation in gene expression. We introduce SAGE-net, a scalable framework for training and evaluating S2F models using personal genomes. While personal genome training improves gene expression prediction accuracy for held-out individuals, performance gains arise primarily from identifying predictive variants rather than learning a cis-regulatory grammar that generalizes across loci. Scalable software will be critical to advancing S2F models for personal genomics.
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Registered trials
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