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

ABSTRACT: 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%.

SUBMITTER: Larrea-Sebal A

PROVIDER: S-EPMC8617597 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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

Larrea-Sebal Asier A Benito-Vicente Asier A Fernandez-Higuero José A JA Jebari-Benslaiman Shifa S Galicia-Garcia Unai U Uribe Kepa B KB Cenarro Ana A Ostolaza Helena H Civeira Fernando F Arrasate Sonia S González-Díaz Humberto H Martín César C

JACC. Basic to translational science 20211122 11

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 ...[more]

PMID: 34869944

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