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
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
Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.
Ryu J et al.
Medrxiv : the Preprint Server for Health Sciences, 2023
37732177 (PubMed )
Supplement: media-3.zip › Supplementary_Tables › TableS7_tiling_screen.xlsx › TableSX_tiling_screen
A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia.
Gratton J et al.
Jacc. Advances, 2023
38938233 (PubMed )
Supplement: mmc1.docx
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
Screening for familial hypercholesterolaemia in childhood: Avon Longitudinal Study of Parents and Children (ALSPAC).
Futema M et al.
Atherosclerosis, 2017
Mutational analysis in UK patients with a clinical diagnosis of familial hypercholesterolaemia: relationship with plasma lipid traits, heart disease risk and utility in relative tracing.
Humphries SE et al.
Journal of Molecular Medicine (berlin, Germany), 2006
Congenital insensitivity to pain with anhidrosis: novel mutations in the TRKA (NTRK1) gene encoding a high-affinity receptor for nerve growth factor.
Mardy S et al.
American Journal of Human Genetics, 1999
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