CardioVar: A Machine Learning Framework for Pathogenicity Prediction of Cardiomyopathy Genetic Variants

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ID: 313810
2026
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Abstract
Abstract Motivation CardioVar is a machine learning framework developed to predict the pathogenicity of cardiomyopathy-associated genetic variants, providing a rapid and disease-focused classification score to support variant interpretation in cardiogenetic workflows. Results This approach supports variant interpretation within cardiac-related genes and may complement existing pan-disease models within a cardiomyopathy-specific context. In prospective case study settings, CardioVar demonstrated an 85% concordance with conventional tertiary analysis results. Availability https://github.com/nibrasissa/CardioVar Supplementary information Supplementary data are available at Bioinformatics online.
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openalex_W7160952476 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nabras Al-Mahrami, Aaisha Albalushi, Fahad Al Hattali, Mashael Al Balushi, Bushra Al Shamsi, Musallam Al-Oraimi, Tuqa Al Lawati, Mohamed Al-Rawahi, Nadia Alhashmi, A Al-Amri
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag135
URL
https://doi.org/10.1093/bioadv/vbag135
Keywords Keywords not found

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