ArticleGenome biology2026
Deep-learning prediction of gene expression from personal genomes.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Tailoring AI and ML models for genotype-by-environment prediction leveraging environmental covariates: A European rye example.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Buffering of gene dosage response curves for human complex traits.Cell genomics · 2026Article
- A scalable approach to investigating sequence-to-function predictions from personal genomes.Nature methods · 2026Article
- Evaluating sequence-to-function deep learning models for ancestry-stratified regulatory variant effect prediction using multi-ancestry blood eQTLs.bioRxiv : the preprint server for biology · 2026Article
- Fine-tuning sequence-to-expression models on personal genome and transcriptome data.Genome biology · 2026Article
- AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.bioRxiv : the preprint server for biology · 2026Article
- In silico genome transplants and the cis-regulatory basis of biodiversity.Trends in genetics : TIG · 2026Review
- Personalized gene expression prediction in the era of deep learning: a review.Briefings in bioinformatics · 2026Review
- Pre-training genomic language model with variants for better modeling functional genomics.NPJ artificial intelligence · 2026Article
- gReLU: a comprehensive framework for DNA sequence modeling and design.Nature methods · 2025Article
- UNICORN: Towards universal cellular expression prediction with a multi-task learning framework.Nature communications · 2025Article
- 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
- AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.Research square · 2025Article
- Flashzoi: an enhanced Borzoi for accelerated genomic analysis.Bioinformatics (Oxford, England) · 2025Article
- Pre-training Genomic Language Model with Variants for Better Modeling Functional Genomics.bioRxiv : the preprint server for biology · 2025Article
- Training deep learning models on personalized genomic sequences improves variant effect prediction.bioRxiv : the preprint server for biology · 2025Article
- GenVarLoader: An accelerated dataloader for applying deep learning to personalized genomics.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundModels that predict gene expression levels from DNA sequence struggle to predict differences between individuals when given their personal genome sequences. These models are generally trained on reference genome sequences, and thus have never observed examples of genetic variation at any locus during training, which may explain their lack of generalizability to personal genome sequences that do contain variation.
resultsWe utilize fine-tuning with personal genomes and matched tissue-specific gene expression values to develop Variformer, a deep sequence-based neural network. Across held-out people, Variformer predicts expression with accuracy that approaches the cis-heritability of most genes and prioritizes genetic variants across the allele frequency spectrum that are enriched for motif disruption and other functional annotations. We highlight how Variformer fails to generalize to unseen genes.
conclusionsOur work suggests that fine-tuning with personal genomes corrects previously reported shortcomings of gene expression prediction across unseen individuals, but does not learn a regulatory grammar that generalizes to unseen loci. Fine-tuned deep expression models thus share similar performance and limitations of state-of-the-art linear models, highlighting a gap for the field.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.