ArticleNature genetics2018
Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.
Article in Nature genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 351 papers, 1 of them a synthesis that pooled it.
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
351 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Leveraging genomic deep learning models for the prediction of noncoding variant effects.Nature genetics · 2026Review
- Spatiotemporal AI-based modeling of gene regulation and variant effects in the developing human brain using Seal.Nature neuroscience · 2026Article
- Mechanisms underlying disease-causing variants in promoters and enhancers.Nature genetics · 2026Review
- 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
- De novo structural variants in autism spectrum disorder disrupt distal regulatory interactions of neuronal genes.Genome research · 2026Article
- Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.Bioinformatics (Oxford, England) · 2026Article
- DeepGeSeq: deep learning library for genomic sequence modeling and analysis.Bioinformatics (Oxford, England) · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Systematic Benchmarking of DNA Sequence Encoding Strategies for Predicting Regulatory Effects of Non-Coding SNPs.International journal of molecular sciences · 2026Article
- Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization.Pharmaceuticals (Basel, Switzerland) · 2026Article
- The Encyclopedia of DNA Elements.bioRxiv : the preprint server for biology · 2026Article
- A chromatin-structure-guided framework for predictive and interpretable regulatory genomics.Briefings in bioinformatics · 2026Article
- DNA-DETR: sequence representation matters in object detection for functional genomic elements.Briefings in bioinformatics · 2026Article
- A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species.Nature methods · 2026Article
- DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.PLoS computational biology · 2026Article
- Insights from adaptive immune regulation for disease resistance breeding in livestock and poultry.Science China. Life sciences · 2026Review
- CodonMoE: DNA language models for codon-dependent mRNA prediction.Bioinformatics (Oxford, England) · 2026Article
- Decoding sequence determinants of gene expression in diverse cellular and disease states.Nature methods · 2026Article
291 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variation. However, this is extremely difficult because of the enormous scale of the noncoding mutation space. We developed a deep learning-based framework, ExPecto, that can accurately predict, ab initio from a DNA sequence, the tissue-specific transcriptional effects of mutations, including those that are rare or that have not been observed. We prioritized causal variants within disease- or trait-associated loci from all publicly available genome-wide association studies and experimentally validated predictions for four immune-related diseases. By exploiting the scalability of ExPecto, we characterized the regulatory mutation space for human RNA polymerase II-transcribed genes by in silico saturation mutagenesis and profiled > 140 million promoter-proximal mutations. This enables probing of evolutionary constraints on gene expression and ab initio prediction of mutation disease effects, making ExPecto an end-to-end computational framework for the in silico prediction of expression and disease risk.
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What OpenQuestion holds
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.