ArticleNucleic acids research2017
Predicting the impact of non-coding variants on DNA methylation.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
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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.
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Who cites it
55 citing papers in PubMed, 101 citations in OpenAlex.
- Decoding the sequence determinants of locus-specific DNA methylation across human tissues.Nature communications · 2026Article
- Systematic Benchmarking of DNA Sequence Encoding Strategies for Predicting Regulatory Effects of Non-Coding SNPs.International journal of molecular sciences · 2026Article
- Epigenetic conditioning improves sequence-based modeling of gene regulation across cell types and alleles.bioRxiv : the preprint server for biology · 2026Article
- Variant-to-function dissection of the 17q21.31 locus resolves ANKRD1 as a convergent regulatory target.BMC medical genomics · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- Advances in Artificial Intelligence (AI) Models and Generative Algorithms Represent a New Paradigm for Genomics Research.International journal of molecular sciences · 2025Review
- Predicting sequence-specific amplification efficiency in multi-template PCR with deep learning.Nature communications · 2025Article
- A Biologically Informed and Efficient DNA Sequence Learner for Predicting Functional Genomics Events.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Deep Learning-Based DNA Methylation Detection in Cervical Cancer Using the One-Hot Character Representation Technique.Diagnostics (Basel, Switzerland) · 2025Article
- UNet with Attention Networks: A Novel Deep Learning Approach for DNA Methylation Prediction in HeLa Cells.Genes · 2025Article
- Epigenetic Impacts of Non-Coding Mutations Deciphered Through Pre-Trained DNA Language Model at Single-Cell Resolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns.3 Biotech · 2024Article
- Learning peptide properties with positive examples only.Digital discovery · 2024Article
- Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences.BMC bioinformatics · 2024Article
- MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation.Theory in biosciences = Theorie in den Biowissenschaften · 2023Article
- DNA language models are powerful predictors of genome-wide variant effects.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Applications for Deep Learning in Epilepsy Genetic Research.International journal of molecular sciences · 2023Review
- On the prediction of non-CG DNA methylation using machine learning.NAR genomics and bioinformatics · 2023Article
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
DNA methylation plays a crucial role in the establishment of tissue-specific gene expression and the regulation of key biological processes. However, our present inability to predict the effect of genome sequence variation on DNA methylation precludes a comprehensive assessment of the consequences of non-coding variation. We introduce CpGenie, a sequence-based framework that learns a regulatory code of DNA methylation using a deep convolutional neural network and uses this network to predict the impact of sequence variation on proximal CpG site DNA methylation. CpGenie produces allele-specific DNA methylation prediction with single-nucleotide sensitivity that enables accurate prediction of methylation quantitative trait loci (meQTL). We demonstrate that CpGenie prioritizes validated GWAS SNPs, and contributes to the prediction of functional non-coding variants, including expression quantitative trait loci (eQTL) and disease-associated mutations. CpGenie is publicly available to assist in identifying and interpreting regulatory non-coding variants.
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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.