ArticleGenome research2016
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks.
Article in Genome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 549 papers.
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Who cites it
549 citing papers in PubMed.
- PROTACs in cancer therapy: targeted degradation of GPX4, PARP and epigenetic regulators.Journal of enzyme inhibition and medicinal chemistry · 2026Review
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- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- GenART: Reading the genome's language with adaptive "words".Cell reports methods · 2026Article
- An atlas of transcription factor cooperation reveals how motif readers shape regulatory output.bioRxiv : the preprint server for biology · 2026Article
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- Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo.Nature genetics · 2026Article
- Cross-Model Uncertainty-Aware Minimal Editing of Cis-Regulatory Elements for Cell-Type-Selective Design.Genes · 2026Article
- Functional genomic dissection and prediction of body size traits in pigs.Genetics, selection, evolution : GSE · 2026Article
- Construction and validation of a phenotypic prediction model for bacterial gentamicin resistance using deep learning with gene sequences.Microbiology spectrum · 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
- Gene regulatory innovations from transposable elements in primate cerebellum development.Nature communications · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.Nucleic acids research · 2026Article
- Positional interpretation of cis-regulatory code and nucleosome organization with deep learning models.Nature communications · 2026Article
- Unlocking the Regulatory Genome: Interpreting the Clinical Impact of Noncoding Variants in Genetic Cardiomyopathies.Circulation. Genomic and precision medicine · 2026Review
- K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.Briefings in bioinformatics · 2026Article
- A scalable approach to investigating sequence-to-function predictions from personal genomes.Nature methods · 2026Article
489 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance-deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.
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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.