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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Detalhes bibliográficos
Autores: Larrea-Sebal, A., Benito-Vicente, A., Fernandez-Higuero, J.A., Jebari-Benslaiman, S., Galicia-Garcia, U., Uribe, K.B., Cenarro, A., Ostolaza, H., Civeira, F., Arrasate, S., González-Díaz, H., Martín, C.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Universidad de Zaragoza
Repositorio:Zaguán. Repositorio Digital de la Universidad de Zaragoza
OAI Identifier:oai:zaguan.unizar.es:110703
Acesso em linha:http://zaguan.unizar.es/record/110703
Access Level:acceso abierto
Descrição
Resumo: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%. © 2021 The Authors

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