ArticleBriefings in bioinformatics2021
DeepCNV: a deep learning approach for authenticating copy number variations.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.Briefings in bioinformatics · 2022Pooled it
- Landscape of copy number variants in Spanish people with dementia.NPJ genomic medicine · 2026Article
- CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data.bioRxiv : the preprint server for biology · 2026Article
- Copy Number Variant Duplications Associated with Essential Tremor.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- Genome wide landscaping of copy number variations for horse inter-breed variability.Animal biotechnology · 2025Article
- PangenomeX: a graph convolutional network-based pangenome framework for unbiased population-scale genomic variation analysis.Briefings in bioinformatics · 2025Article
- Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision.Current issues in molecular biology · 2024Article
- A Bioinformatics Toolkit for Next-Generation Sequencing in Clinical Oncology.Current issues in molecular biology · 2023Review
- Chromothripsis detection with multiple myeloma patients based on deep graph learning.Bioinformatics (Oxford, England) · 2023Article
- SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data.BMC bioinformatics · 2023Article
- Rare recurrent copy number variations in metabotropic glutamate receptor interacting genes in children with neurodevelopmental disorders.Journal of neurodevelopmental disorders · 2023Article
- Artificial intelligence-based approaches for the detection and prioritization of genomic mutations in congenital surgical diseases.Frontiers in pediatrics · 2023Review
- A novel scatterplot-based method to detect copy number variation (CNV).Frontiers in genetics · 2023Article
- Accurate in silico confirmation of rare copy number variant calls from exome sequencing data using transfer learning.Nucleic acids research · 2022Article
- Artificial intelligence: A powerful paradigm for scientific research.Innovation (Cambridge (Mass.)) · 2021Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably high false positive rate. A common practice is to have human experts manually review original CNV calls for filtering false positives before further downstream analysis or experimental validation. Here, we propose DeepCNV, a deep learning-based tool, intended to replace human experts when validating CNV calls, focusing on the calls made by one of the most accurate CNV callers, PennCNV. The sophistication of the deep neural network algorithm is enriched with over 10 000 expert-scored samples that are split into training and testing sets. Variant confidence, especially for CNVs, is a main roadblock impeding the progress of linking CNVs with the disease. We show that DeepCNV adds to the confidence of the CNV calls with an optimal area under the receiver operating characteristic curve of 0.909, exceeding other machine learning methods. The superiority of DeepCNV was also benchmarked and confirmed using an experimental wet-lab validation dataset. We conclude that the improvement obtained by DeepCNV results in significantly fewer false positive results and failures to replicate the CNV association results.
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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.