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
Achieving high-sensitivity for clinical applications using augmented exome sequencing.
Patwardhan A et al.
Genome Medicine, 2015
26269718 (PubMed )
Supplement: 13073_2015_197_MOESM8_ESM.xlsx › Sheet1
Mutation detection rate and spectrum in familial hypercholesterolaemia patients in the UK pilot cascade project.
Taylor A et al.
Clinical Genetics, 2010
Multiplex ARMS analysis to detect 13 common mutations in familial hypercholesterolaemia.
Taylor A et al.
Clinical Genetics, 2007
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