ArticleGenome biology2019
NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- NCBoost v2: a classifier for non-coding single-nucleotide variants in Mendelian diseases.Bioinformatics (Oxford, England) · 2026Article
- CASER: A semi-supervised model with multi-omics data integration prioritizes cancer-associated epigenetic regulator genes.PLoS computational biology · 2026Article
- COBT: a gene-based rare variant burden test for case-only study designs using aggregated genotypes from public reference cohorts.Genome medicine · 2026Article
- Integrating natural and engineered genetic variations to decode regulatory influence on blood traits.Cell reports · 2026Article
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Clinical Variant Databases and Machine Learning Prediction Supporting Genomic Medicine.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Criss cross heart: an outflow tract defect?Orphanet journal of rare diseases · 2025Article
- Stratifying variant deleteriousness and trait-modulating effect under human recent adaptation using the FIND model.Genome biology · 2025Article
- Federated learning for the pathogenicity annotation of genetic variants in multi-site clinical settings.Bioinformatics (Oxford, England) · 2025Article
- Clinical and data-driven optimization of Genomiser for rare disease patients: experience from the Hong Kong Genome Project.Briefings in bioinformatics · 2025Article
- Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets.Communications biology · 2025Article
- PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis.Genetic epidemiology · 2025Article
- Quantifying the regulatory potential of genetic variants via a hybrid sequence-oriented model with SVEN.Nature communications · 2024Article
- Regional-specific calibration enables application of computational evidence for clinical classification of 5' cis-regulatory variants in Mendelian disease.American journal of human genetics · 2024Article
- Network embedding framework for driver gene discovery by combining functional and structural information.BMC genomics · 2023Article
- A non-coding variant in the Kozak sequence of RARS2 strongly decreases protein levels and causes pontocerebellar hypoplasia.BMC medical genomics · 2023Article
- TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.Bioinformatics (Oxford, England) · 2022Article
- Recommendations for clinical interpretation of variants found in non-coding regions of the genome.Genome medicine · 2022Article
- Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms.Genes · 2022Article
- Classification of non-coding variants with high pathogenic impact.PLoS genetics · 2022Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
State-of-the-art methods assessing pathogenic non-coding variants have mostly been characterized on common disease-associated polymorphisms, yet with modest accuracy and strong positional biases. In this study, we curated 737 high-confidence pathogenic non-coding variants associated with monogenic Mendelian diseases. In addition to interspecies conservation, a comprehensive set of recent and ongoing purifying selection signals in humans is explored, accounting for lineage-specific regulatory elements. Supervised learning using gradient tree boosting on such features achieves a high predictive performance and overcomes positional bias. NCBoost performs consistently across diverse learning and independent testing data sets and outperforms other existing reference methods.
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