Associated Papers
High-throughput evaluation of genetic variants with prime editing sensor libraries.
Gould SI et al.
Nature Biotechnology, 2025
Somatic mutations in 3929 HPV positive cervical cells associated with infection outcome and HPV type.
Pinheiro M et al.
Nature Communications, 2024
39266536 (PubMed )
Supplement: 41467_2024_51713_MOESM5_ESM.xlsx › Data_2
Standing in the canine precision medicine knowledge gap: Improving annotation of canine cancer genomic biomarkers through systematic comparative analysis of human cancer mutations in COSMIC.
Sakthikumar S et al.
Veterinary and Comparative Oncology, 2023
37248814 (PubMed )
Supplement: supplement_1.xlsx › Suppl._Table_6
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.
Ben-Cohen G et al.
Briefings in Bioinformatics, 2022
Finding driver mutations in cancer: Elucidating the role of background mutational processes.
Brown AL et al.
Plos Computational Biology, 2019
31034466 (PubMed )
Supplement: pcbi.1006981.s010.xlsx › final_benchmark
Machine Learning Classification and Structure-Functional Analysis of Cancer Mutations Reveal Unique Dynamic and Network Signatures of Driver Sites in Oncogenes and Tumor Suppressor Genes.
Agajanian S et al.
Journal of Chemical Information and Modeling, 2018
Clinical applicability and cost of a 46-gene panel for genomic analysis of solid tumours: Retrospective validation and prospective audit in the UK National Health Service.
Hamblin A et al.
Plos Medicine, 2017
28196074 (PubMed )
Supplement: pmed.1002230.s004.xlsx › Non-Small_Cell_Lung_Carcinoma
Protein interaction between p53 and Δ113p53 is required for the anti-apoptotic function of Δ113p53.
Ou Z et al.
Journal of Genetics and Genomics = Yi Chuan Xue Bao, 2014
The relationship among p53 oligomer formation, structure and transcriptional activity using a comprehensive missense mutation library.
Kawaguchi T et al.
Oncogene, 2005
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