Associated Papers
Clinical and genomic features of Lynch syndrome differ by tumor site and disease spectrum.
Li S et al.
Nature Communications, 2025
41261115 (PubMed )
Supplement: 41467_2025_65164_MOESM1_ESM.pdf
Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.
Ahmad RM et al.
Briefings in Bioinformatics, 2025
41263939 (PubMed )
Supplement: supplementary_materials_bbaf611_1_.zip › Supplementary_materials_bbaf611 › Supplementary_Table_S8.xlsx › Sheet1
ctDNA-based liquid biopsy reveals wider mutational profile with therapy resistance and metastasis susceptibility signatures in early-stage breast cancer patients.
Bharde A et al.
The Journal of Liquid Biopsy, 2025
40027235 (PubMed )
Supplement: mmc4.xlsx › Sheet1
vaRHC: an R package for semi-automation of variant classification in hereditary cancer genes according to ACMG/AMP and gene-specific ClinGen guidelines.
Munté E et al.
Bioinformatics (oxford, England), 2023
36916756 (PubMed )
Supplement: btad128_supplementary_data.zip › btad128_Supplementary_Data › Supplementary_tables10022023.xlsx › ST15-PS1_PM5_hypothethical_vars
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
30212499 (PubMed )
Supplement: pone.0203553.s008.xlsx › Variants
Exome sequencing covers >98% of mutations identified on targeted next generation sequencing panels.
LaDuca H et al.
Plos One, 2017
28152038 (PubMed )
Supplement: pone.0170843.s003.xlsx › S3_Table
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