ArticleNature genetics2023
Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.
Article in Nature genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
98 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
- EPIC: An open community challenge for sequence-based prediction of transcription initiation in five non-model metazoans.bioRxiv : the preprint server for biology · 2026Article
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Bridging precision agriculture and human medicine through comparative genetics.Nature reviews. Genetics · 2026Review
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- GUANinE v1.1 reveals complementarity of supervised and genomic language models.Bioinformatics (Oxford, England) · 2026Article
- xDecoder unlocks the potential of genomic foundation models for few-shot personal gene expression prediction.Molecular systems biology · 2026Article
- Deconvolution-derived cell-type expression targets for personal genome sequence-to-expression prediction.bioRxiv : the preprint server for biology · 2026Article
- Pangenome-based human genome analysis improves trait association and genomic prediction.bioRxiv : the preprint server for biology · 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
- Transcription clusters and developmental pathways - nature, nurture, noise.Journal of cell science · 2026Review
- Challenges in predicting chromatin accessibility differences between species.NAR genomics and bioinformatics · 2026Article
- Fine-tuning sequence-to-expression models on personal genome and transcriptome data.Genome biology · 2026Article
- Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning.Nature communications · 2026Article
- AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.bioRxiv : the preprint server for biology · 2026Article
- ModelingbioRxiv : the preprint server for biology · 2026Article
- Genetic background shapes AI-predicted variant effects.bioRxiv : the preprint server for biology · 2026Article
- Beyond the baseline: mapping the context-specific regulatory landscape of disease.Trends in genetics : TIG · 2026Review
38 more citing papers are in PubMed but not listed here.
Corrections and comments
- Update of
Authors and funding
9 authors.
Funding
Abstract
Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks
Indexed as
Identifiers
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Registered trials
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.