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
Mutation type classification and pathogenicity assignment of sixteen missense variants located in the EGF-precursor homology domain of the LDLR.
Galicia-Garcia U et al.
Scientific Reports, 2020
32015373 (PubMed )
Supplement: 41598_2020_58734_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
Development of a high-resolution melting method for mutation detection in familial hypercholesterolaemia patients.
Whittall RA et al.
Annals of Clinical Biochemistry, 2010
Evaluation of high-resolution melting analysis for screening the LDL receptor gene.
Laurie AD et al.
Clinical Biochemistry, 2009
Multiplex MassARRAY spectrometry (iPLEX) produces a fast and economical test for 56 familial hypercholesterolaemia-causing mutations.
Wright WT et al.
Clinical Genetics, 2008
Multiplex ARMS analysis to detect 13 common mutations in familial hypercholesterolaemia.
Taylor A et al.
Clinical Genetics, 2007
A molecular genetic service for diagnosing individuals with familial hypercholesterolaemia (FH) in the United Kingdom.
Heath KE et al.
European Journal of Human Genetics : Ejhg, 2001
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