Associated Papers
Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease.
Serghini A et al.
Human Molecular Genetics, 2024
37883464 (PubMed )
Supplement: supplementary_tables_marked_edit_ddad181.xlsx › Supplementary_Table_1
Computational analysis of protein-protein interactions of cancer drivers in renal cell carcinoma.
Pei J et al.
Febs Open Bio, 2024
37964489 (PubMed )
Supplement: FEB4-14-112-s002.xlsx › AlphaFold_models
Application of Genomic Sequencing to Refine Patient Stratification for Adjuvant Therapy in Renal Cell Carcinoma.
Vasudev NS et al.
Clinical Cancer Research : an Official Journal of the American Association for Cancer Research, 2023
36815791 (PubMed )
Supplement: ccr-22-1936_supplementary_table_s2_suppts2.xlsx › Sheet1
Genotype-phenotype relations of the von Hippel-Lindau tumor suppressor inferred from a large-scale analysis of disease mutations and interactors.
Minervini G et al.
Plos Computational Biology, 2019
30943211 (PubMed )
Supplement: pcbi.1006478.s001.xlsx › AlignGVGD
Characterization of VHL missense mutations in sporadic clear cell renal cell carcinoma: hotspots, affected binding domains, functional impact on pVHL and therapeutic relevance.
Razafinjatovo C et al.
Bmc Cancer, 2016
27530247 (PubMed )
Supplement: 12885_2016_2688_MOESM3_ESM.xlsx › Sheet1
An integrated computational approach can classify VHL missense mutations according to risk of clear cell renal carcinoma.
Gossage L et al.
Human Molecular Genetics, 2014
24969085 (PubMed )
Supplement: supp_ddu321_ddu321supp.docx
VHL gene mutations and their effects on hypoxia inducible factor HIFα: identification of potential driver and passenger mutations.
Rechsteiner MP et al.
Cancer Research, 2011
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