MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants

Untreated familial hypercholesterolemia (FH) leads to atherosclerosis and early cardiovascular disease. Mutations in the low-density lipoprotein receptor (LDLr) gene constitute the major cause of FH, and the high number of mutations already described in the LDLr makes necessary cascade screening or...

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Detalles Bibliográficos
Autores principales: Larrea-Sebal, Asier, Benito-Vicente, Asier, Fernandez-Higuero, José A., Jebari-Benslaiman, Shifa, Galicia-Garcia, Unai, Uribe, Kepa B., Cenarro, Ana, Ostolaza, Helena, Civeira, Fernando, Arrasate, Sonia, González-Díaz, Humberto, Martín, César
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Preclinical Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8617597/
https://www.ncbi.nlm.nih.gov/pubmed/34869944
http://dx.doi.org/10.1016/j.jacbts.2021.08.009
Descripción
Sumario:Untreated familial hypercholesterolemia (FH) leads to atherosclerosis and early cardiovascular disease. Mutations in the low-density lipoprotein receptor (LDLr) gene constitute the major cause of FH, and the high number of mutations already described in the LDLr makes necessary cascade screening or in vitro functional characterization to provide a definitive diagnosis. Implementation of high-predicting capacity software constitutes a valuable approach for assessing pathogenicity of LDLr variants to help in the early diagnosis and management of FH disease. This work provides a reliable machine learning model to accurately predict the pathogenicity of LDLr missense variants with specificity of 92.5% and sensitivity of 91.6%.

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