Associated Papers
MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants.
Larrea-Sebal A et al.
Jacc. Basic to Translational Science, 2021
Advancing Prediction of Pathogenicity of Familial Hypercholesterolemia LDL Receptor Commonest Variants With Machine Learning Models.
Santos RD
Jacc. Basic to Translational Science, 2021
Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts.
Cirulli ET et al.
Nature Communications, 2020
31992710 (PubMed )
Supplement: 41467_2020_14288_MOESM1_ESM.pdf
Molecular modeling of LDLR aids interpretation of genomic variants.
Klee EW et al.
Journal of Molecular Medicine (berlin, Germany), 2019
30778614 (PubMed )
Supplement: 109_2019_1755_MOESM1_ESM.xlsx › TableS1
Validation of LDLr Activity as a Tool to Improve Genetic Diagnosis of Familial Hypercholesterolemia: A Retrospective on Functional Characterization of LDLr Variants.
Benito-Vicente A et al.
International Journal of Molecular Sciences, 2018
The UCL low-density lipoprotein receptor gene variant database: pathogenicity update.
Leigh S et al.
Journal of Medical Genetics, 2017
Functional characterization of splicing and ligand-binding domain variants in the LDL receptor.
Etxebarria A et al.
Human Mutation, 2012
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