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

Overview

Journal JACC Basic Transl Sci

Date 2021 Dec 6

PMID 34869944

Citations 12

Authors

Asier Larrea-Sebal

Asier Benito-Vicente

Jose A Fernandez-Higuero

Shifa Jebari-Benslaiman

Unai Galicia-Garcia

Kepa B Uribe

Ana Cenarro

Helena Ostolaza

Fernando Civeira

Sonia Arrasate

Humberto Gonzalez-Diaz

Cesar Martin

Affiliations

Soon will be listed here.

Abstract

Untreated familial hypercholesterolemia (FH) leads to atherosclerosis and early cardiovascular disease. Mutations in the low-density lipoprotein receptor () gene constitute the major cause of FH, and the high number of mutations already described in the 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 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 missense variants with specificity of 92.5% and sensitivity of 91.6%.

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