Associated Papers
Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?
Stevens CAT et al.
Journal of the American Heart Association, 2024
38879446 (PubMed )
Supplement: JAH3-13-e034434-s001.pdf
Genotype-phenotype correlation in a large cohort of pediatric patients with heterozygous and homozygous familial hypercholesterolemia.
Reijman MD et al.
Current Opinion in Lipidology, 2023
36752612 (PubMed )
Supplement: colip-34-287-s001.xlsx › Suppl_Table_1_-_variants__HeFH_
Modelling a two-stage adult population screen for autosomal dominant familial hypercholesterolaemia: cross-sectional analysis within the UK Biobank.
Gratton J et al.
Bmj Public Health, 2023
40017868 (PubMed )
Supplement: bmjph-1-1-s001.pdf
Low-Cost High-Throughput Genotyping for Diagnosing Familial Hypercholesterolemia.
Ibrahim S et al.
Circulation. Genomic and Precision Medicine, 2023
37675602 (PubMed )
Supplement: hcg-16-462-s001.pdf
A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia.
Gratton J et al.
Jacc. Advances, 2023
38938233 (PubMed )
Supplement: mmc1.docx
Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.
Halford JL et al.
Nature Communications, 2022
36042188 (PubMed )
Supplement: 41467_2022_32009_MOESM4_ESM.xlsx › Supplementary_Data_2
The UMD-LDLR database: additions to the software and 490 new entries to the database.
Villéger L et al.
Human Mutation, 2002
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