ArticleNature communications2020
Evaluating the informativeness of deep learning annotations for human complex diseases.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Neutrophil-derived S100A8/A9 impairs megakaryocyte maturation in immune thrombocytopenia.Nature communications · 2026Article
- Fine-mapping candidate neuropsychiatric regulatory variants using cell type-aware comparative genomics.bioRxiv : the preprint server for biology · 2026Article
- Predicting disease-specific histone modifications and functional effects of non-coding variants by leveraging DNA language models.Genome biology · 2026Article
- Iterative improvement of deep learning models using synthetic regulatory genomics.Genome research · 2025Article
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- Multi-dimensional annotation of porcine variants using genomic and epigenomic features in pigs.BMC biology · 2025Article
- Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain.medRxiv : the preprint server for health sciences · 2025Article
- Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics.bioRxiv : the preprint server for biology · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation.BioData mining · 2024Article
- Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Current genomic deep learning models display decreased performance in cell type-specific accessible regions.Genome biology · 2024Article
- Current genomic deep learning models display decreased performance in cell type specific accessible regions.bioRxiv : the preprint server for biology · 2024Article
- GUANinE v1.0: Benchmark Datasets for Genomic AI Sequence-to-Function Models.bioRxiv : the preprint server for biology · 2024Article
- Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation.Research square · 2024Article
- Multi-omics cannot replace sample size in genome-wide association studies.Genes, brain, and behavior · 2023Article
- Correcting gradient-based interpretations of deep neural networks for genomics.Genome biology · 2023Article
- EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations.Genome biology · 2023Article
- Article
- Evaluating deep learning for predicting epigenomic profiles.Nature machine intelligence · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learning models, DeepSEA and Basenji, by applying stratified LD score regression to 41 diseases and traits (average N = 320K), conditioning on a broad set of coding, conserved and regulatory annotations. We aggregated annotations across all (respectively blood or brain) tissues/cell-types in meta-analyses across all (respectively 11 blood or 8 brain) traits. The annotations were highly enriched for disease heritability, but produced only limited conditionally significant results: non-tissue-specific and brain-specific Basenji-H3K4me3 for all traits and brain traits respectively. We conclude that deep learning models have yet to achieve their full potential to provide considerable unique information for complex disease, and that their conditional informativeness for disease cannot be inferred from their accuracy in predicting regulatory annotations.
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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.