CNValidatron: accurate and efficient validation of PennCNV calls using computer vision
Visual inspection is the only effective validation approach for CNV calls. Our model is capable of automating this task at scale with very high accuracy, as shown by testing both within-sample and out-of-sample. The software is available as an R package at https://github.com/SinomeM/CNValidatron_fl .
Authors
- Simone Montalbano (ORCID: https://orcid.org/0000-0002-9846-0560)
- Jesper R. Gådin (ORCID: https://orcid.org/0000-0002-9210-9534)
- Thomas Werge
- Hreinn Stefansson
- Gudbjorn F. Jonsson
- G. Bragi Walters
- Daniel F. Gudbjartsson
Institutions
- University of Copenhagen (DK)
- deCODE Genetics (Iceland) (IS)
- University of Iceland (IS)
- Sankt Hans Hospital (DK)
- Amager Hospital (DK)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2026-01-23
- DOI
- https://doi.org/10.1186/s12859-026-06375-6
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health