ArticleNucleic acids research2018
DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed, 121 citations in OpenAlex.
- Leveraging genomic deep learning models for the prediction of noncoding variant effects.Nature genetics · 2026Review
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- Mechanisms underlying disease-causing variants in promoters and enhancers.Nature genetics · 2026Review
- Decoding adaptive immunity: advanced strategies in T and B cell repertoire analysis.Journal of translational medicine · 2026Review
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- 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
- Sucrose as a key nutritional marker distinguishing vegetable and grain soybeans, regulated byHorticulture research · 2025Article
- Emerging frontiers in SERS-integrated optical waveguides: advancing portable and ultra-sensitive detection for trace liquid analysis.Light, science & applications · 2025Review
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- Fundamentals for predicting transcriptional regulations from DNA sequence patterns.Journal of human genetics · 2024Review
- Progress and opportunities of foundation models in bioinformatics.Briefings in bioinformatics · 2024Review
- Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.Briefings in bioinformatics · 2024Review
- Deep learning with a small dataset predicts chromatin remodelling contribution to winter dormancy of apple axillary buds.Tree physiology · 2024Article
- Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases.Frontiers in bioscience (Scholar edition) · 2024Review
- Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.Briefings in bioinformatics · 2024Article
- Predictive analyses of regulatory sequences with EUGENe.Nature computational science · 2023Article
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
- Deep learning-empowered crop breeding: intelligent, efficient and promising.Frontiers in plant science · 2023Article
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 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifying variants that disrupt TF binding and lead to human diseases remains a great challenge. To address this, we implement sequence-based deep learning models that accurately predict the TF binding intensities to given DNA sequences. In addition to accurately classifying TF-DNA binding or unbinding, our models are capable of accurately predicting real-valued TF binding intensities by leveraging large-scale TF ChIP-seq data. The changes in the TF binding intensities between the altered sequence and the reference sequence reflect the degree of functional impact for the variant. This enables us to develop the tool DeFine (Deep learning based Functional impact of non-coding variants evaluator, http://define.cbi.pku.edu.cn) with improved performance for assessing the functional impact of non-coding variants including SNPs and indels. DeFine accurately identifies the causal functional non-coding variants from disease-associated variants in GWAS. DeFine is an effective and easy-to-use tool that facilities systematic prioritization of functional 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.