ReviewNature genetics2019
A primer on deep learning in genomics.
Review in Nature genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 392 papers, 2 of them syntheses 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
392 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pharmacogenomic scores in psychiatry: systematic review of current evidence.Translational psychiatry · 2024Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Deep learning enabled prediction of nuclear lamina-associated chromatin.Patterns (New York, N.Y.) · 2026Article
- Harnessing microRNA regulatory networks for engineering climate resilience and nutritional enhancement in millets.Stress biology · 2026Review
- Construction and validation of a phenotypic prediction model for bacterial gentamicin resistance using deep learning with gene sequences.Microbiology spectrum · 2026Article
- The use of artificial intelligence in advancing molecular biology in Africa: a narrative review.Molecular genetics and genomics : MGG · 2026Review
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.Briefings in bioinformatics · 2026Article
- Deep learning based attention enhanced phylogenetic radial basis function networks (AE-PRBFN) for genomic codon usage classification across species.Scientific reports · 2026Article
- The evolution of AI-integrated genome editing and its challenges.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Review
- Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.NAR genomics and bioinformatics · 2026Article
- CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.BMC genomics · 2026Article
- Unraveling Atherosclerosis through Multi-omics: Systematic Insights into the Unique Applications and Clinical Perspectives.Current atherosclerosis reports · 2026Review
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.Briefings in bioinformatics · 2026Review
- Automatic radiation-free evaluation of Cobb angle for spinal curvature based on fringe projection profilometry and deep learning technology.Spine deformity · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- GGAR: gradient guided adaptive regularization enhances deep learning classification of brassica species using codon usage bias.BMC bioinformatics · 2026Article
- Application of artificial intelligence in lung cancer diagnosis, therapy, and prognosis.BMC medical genomics · 2026Review
- DcSE: an improved densenet with enhanced attention fusion for super-enhancer prediction.BMC genomics · 2026Article
332 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.
Indexed as
Identifiers
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.