ArticleGenome biology2017
McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.
Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- RWDisEnh+: Enhancing disease-enhancer association prediction through multiplex-heterogeneous networks.PloS one · 2026Article
- Prediction of target genes and functional types of cis-regulatory modules in the human genome reveals their distinct properties.BMC biology · 2025Article
- SOX2-driven enhancer landscape defines the transcriptional architecture of retinogenesis.Development (Cambridge, England) · 2024Article
- Leveraging Machine Learning for Predicting Circadian Transcription in mRNAs and lncRNAs.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024Article
- Article
- Integrative approaches based on genomic techniques in the functional studies on enhancers.Briefings in bioinformatics · 2023Review
- Prediction accuracy of regulatory elements from sequence varies by functional sequencing technique.Frontiers in cellular and infection microbiology · 2023Article
- Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data.Genome biology · 2022Article
- Bluster or Lustre: Can AI Improve Crops and Plant Health?Plants (Basel, Switzerland) · 2021Review
- Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.Briefings in bioinformatics · 2021Article
- Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Understanding 3D genome organization by multidisciplinary methods.Nature reviews. Molecular cell biology · 2021Review
- Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study.International urology and nephrology · 2021Article
- Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data.BMC genomics · 2021Article
- Chromatin accessibility profiling methods.Nature reviews. Methods primers · 2021Article
- Article
- Transcriptional network dynamics during the progression of pluripotency revealed by integrative statistical learning.Nucleic acids research · 2020Article
- Integrative prediction of gene expression with chromatin accessibility and conformation data.Epigenetics & chromatin · 2020Article
- Exploring 3D chromatin contacts in gene regulation: The evolution of approaches for the identification of functional enhancer-promoter interaction.Computational and structural biotechnology journal · 2020Review
- Computational Biology Solutions to Identify Enhancers-target Gene Pairs.Computational and structural biotechnology journal · 2019Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene expression patterns based on enriched sequence features. Predicted expression patterns were 73-98% accurate, predicted assignments showed strong Hi-C interaction enrichment, enhancer-associated histone modifications were evident, and known functional motifs were recovered. Our model provides a general framework to link globally identified enhancers to targets and contributes to deciphering the regulatory genome.
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Registered trials
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