ReviewNature reviews. Genetics2019
Deep learning: new computational modelling techniques for genomics.
Review in Nature reviews. Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 560 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
560 citing papers in PubMed, 1 synthesis or guideline pooled it, 1,180 citations in OpenAlex.
- Pharmacogenomic scores in psychiatry: systematic review of current evidence.Translational psychiatry · 2024Pooled it
- Article
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.Nature methods · 2026Article
- A statistical framework for disease classification with scRNA-Seq Data.bioRxiv : the preprint server for biology · 2026Article
- Cross-Model Uncertainty-Aware Minimal Editing of Cis-Regulatory Elements for Cell-Type-Selective Design.Genes · 2026Article
- Integrative multi-omics profiling and machine learning reveal enhancer RNA signatures for early detection and prognostic stratification in breast cancer.NAR cancer · 2026Article
- Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases.Briefings in bioinformatics · 2026Article
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- A Comprehensive Comparative Analysis of Sequence-Based Deep Learning Models for Single-Cell Genomics.Cell proliferation · 2026Article
- miRNA-mRNA Interaction Network Analysis in Alzheimer's Disease for Biomarker Discovery.Journal of molecular neuroscience : MN · 2026Article
- A deep learning model captures position-specific effects of plant regulatory sequences and suggests genes under complex regulation.Plant physiology · 2026Article
- Integrated Analysis of SNPs and Structural Variations via High-depth Whole-genome Sequencing Reveals the Genetic Architecture and Optimizes Genomic Prediction in Chickens.Poultry science · 2026Article
- Embracing non-linearity in human ageing.Nature reviews. Genetics · 2026Review
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Article
- AI-Guided Long Non-Coding RNA Target Discovery for Precision Medicine: Integrating GWAS, Multi-Omics, Experimental Validation, and RNA Therapeutics.Biomedicines · 2026Review
- Locus-Scale Massively Parallel Reporter Assays.bioRxiv : the preprint server for biology · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Integrating Mutation-Derived and Expression Features from Single-Cell RNA Sequencing: Pitfalls of Standard Cross-Validation in Small-Cohort Settings.International journal of molecular sciences · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
500 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
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.
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.