ArticleHuman mutation2017
Predicting enhancer activity and variant impact using gkm-SVM.
Article in Human mutation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of massively parallel reporter assays enables prediction of regulatory function across cell types.Human mutation · 2019Pooled it
- Non-local modeling of enhancer-promoter interactions, a correspondence on "LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions".Applied intelligence (Dordrecht, Netherlands) · 2025Article
- Enhancer reprogramming: critical roles in cancer and promising therapeutic strategies.Cell death discovery · 2025Review
- Functional Validation of Noncoding Variants Associated With Nonsyndromic Orofacial Cleft.Human mutation · 2025Review
- Review
- Gapped-kmer sequence modeling robustly identifies regulatory vocabularies and distal enhancers conserved between evolutionarily distant mammals.Nature communications · 2024Article
- Allele-specific binding variants causing ChIP-seq peak height of histone modification are not enriched in expression QTL annotations.Genetics, selection, evolution : GSE · 2024Article
- Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer.Genome research · 2024Article
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- 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
- Deep molecular learning of transcriptional control of a synthetic CRE enhancer and its variants.iScience · 2024Article
- MicroRNAs provide negative feedback and stability in gene regulatory network models of cell-state transitions.Frontiers in epigenetics and epigenomics · 2024Article
- Article
- Leveraging massively parallel reporter assays for evolutionary questions.Genome biology · 2023Review
- Systematic assays and resources for the functional annotation of non-coding variants.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2022Article
- Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework.PLoS computational biology · 2022Article
- Multiplexed functional genomic assays to decipher the noncoding genome.Human molecular genetics · 2022Article
- Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.Translational vision science & technology · 2022Article
- SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model.Genes · 2022Article
- Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome.Nucleic acids research · 2022Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
We participated in the Critical Assessment of Genome Interpretation eQTL challenge to further test computational models of regulatory variant impact and their association with human disease. Our prediction model is based on a discriminative gapped-kmer SVM (gkm-SVM) trained on genome-wide chromatin accessibility data in the cell type of interest. The comparisons with massively parallel reporter assays (MPRA) in lymphoblasts show that gkm-SVM is among the most accurate prediction models even though all other models used the MPRA data for model training, and gkm-SVM did not. In addition, we compare gkm-SVM with other MPRA datasets and show that gkm-SVM is a reliable predictor of expression and that deltaSVM is a reliable predictor of variant impact in K562 cells and mouse retina. We further show that DHS (DNase-I hypersensitive sites) and ATAC-seq (assay for transposase-accessible chromatin using sequencing) data are equally predictive substrates for training gkm-SVM, and that DHS regions flanked by H3K27Ac and H3K4me1 marks are more predictive than DHS regions alone.
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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.