ArticleGenome medicine2021
X-CNV: genome-wide prediction of the pathogenicity of copy number variations.
Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 63 citations in OpenAlex.
- CNV-Hub: an integrated web-based platform for CNV classification and interpretation using multi-algorithm consensus.NAR genomics and bioinformatics · 2026Article
- Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation.Global medical genetics · 2026Article
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Horizon: CNV interpretation through rapid automated ACMG-aligned pathogenicity analysis.Human genetics · 2026Article
- Copy Number Variant Duplications Associated with Essential Tremor.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- Article
- Structural variants in the 3D genome as drivers of disease.Nature reviews. Genetics · 2025Review
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- Application and clinical utility assessment of natural language processing-based software for copy-number variants interpretation.Journal of translational medicine · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Patterns and variations of copy number alterations in acute myeloid leukemia: insights from the LeukAtlas database.Leukemia · 2025Article
- Systematic assessment of structural variant annotation tools for genomic interpretation.Life science alliance · 2025Article
- Identification of novel variants underlying non-syndromic primary ovarian insufficiency using a targeted NGS gene panel.Frontiers in endocrinology · 2025Article
- Quantifying the regulatory potential of genetic variants via a hybrid sequence-oriented model with SVEN.Nature communications · 2024Article
- CNVoyant a machine learning framework for accurate and explainable copy number variant classification.Scientific reports · 2024Article
- BCORL1, POF1B, and USP9X copy number variation in women with idiopathic diminished ovarian reserve.Journal of assisted reproduction and genetics · 2024Article
- CNVoyant: A Highly Performant and Explainable Multi-Classifier Machine Learning Approach for Determining the Clinical Significance of Copy Number Variants.Research square · 2024Article
- Understanding genetic variability: exploring large-scale copy number variants through non-invasive prenatal testing in European populations.BMC genomics · 2024Article
- PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.Nature communications · 2023Article
- PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success.
resultsWe have developed a novel computational framework X-CNV ( www.unimd.org/XCNV ), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups.
conclusionsThe ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening.
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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.