Associated Papers
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
Establishing the First Genetic Variant Registry for Breast and Ovarian Cancer in Colombia: Insights and Implications.
Deugd R et al.
Diseases (basel, Switzerland), 2025
40710012 (PubMed )
Supplement: diseases-13-00222-s001.zip › diseases-3616972-supplementary.pdf
SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion.
Canson DM et al.
Bioinformatics (oxford, England), 2023
37021934 (PubMed )
Supplement: btad179_supplementary_data.zip › SAI10kcalc_supp_tables_final.xlsx › S1.variants
APPLICATION OF THE ACMG/AMP FRAMEWORK TO CAPTURE EVIDENCE RELEVANT TO PREDICTED AND OBSERVED IMPACT ON SPLICING: RECOMMENDATIONS FROM THE CLINGEN SVI SPLICING SUBGROUP.
Walker LC et al.
Medrxiv : the Preprint Server for Health Sciences, 2023
36865205 (PubMed )
Supplement: media-10.xlsx › Table_S2
Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.
G V et al.
Frontiers in Genetics, 2022
36561320 (PubMed )
Supplement: Table2.xlsx › BRCA2
Role of Splicing Regulatory Elements and In Silico Tools Usage in the Identification of Deep Intronic Splicing Variants in Hereditary Breast/Ovarian Cancer Genes.
Moles-Fernández A et al.
Cancers, 2021
34283047 (PubMed )
Supplement: cancers-13-03341-s001.zip › 2_Supplementary_table_S2A_2B.xlsx › Suppl_Table_2A
Germline Pathogenic Variants in 7636 Japanese Patients With Prostate Cancer and 12 366 Controls.
Momozawa Y et al.
Journal of the National Cancer Institute, 2020
31214711 (PubMed )
Supplement: djz124_supplementary_data.zip › djz124-Suppl_Data › Supplementary_Tables_5_and_6_060619.xlsx › Supplementary_Table_6
REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification.
Tian Y et al.
Scientific Reports, 2019
31484976 (PubMed )
Supplement: 41598_2019_49224_MOESM2_ESM.xlsx › Sheet1
A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity.
Shamsani J et al.
Bioinformatics (oxford, England), 2019
30475984 (PubMed )
Supplement: bty960_supplementary_data.zip › bty960-Suppl_data › Supplementary_Table_S1.xlsx › Sheet1
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
30212499 (PubMed )
Supplement: pone.0203553.s008.xlsx › Variants
In silico, in vitro and case-control analyses as an effective combination for analyzing BRCA1 and BRCA2 unclassified variants in a population-based sample.
Rodríguez-Balada M et al.
Cancer Genetics, 2016
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