Associated Papers
Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples.
Eberth S et al.
Bmc Research Notes, 2025
39955561 (PubMed )
Supplement: 13104_2025_7140_MOESM2_ESM.xlsx › cosmic_mut_bc_mut_gatk_hc_dbsnp
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
Prevalence of FH-Causing Variants and Impact on LDL-C Concentration in European, South Asian, and African Ancestry Groups of the UK Biobank-Brief Report.
Gratton J et al.
Arteriosclerosis, Thrombosis, and Vascular Biology, 2023
37409534 (PubMed )
Supplement: atv-43-1737-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
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
Select a paper to view details