Associated Papers
Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.
Ahmad RM et al.
Briefings in Bioinformatics, 2025
VarMeter2: An enhanced structure-based method for predicting pathogenic missense variants through Mahalanobis distance.
Ohno S et al.
Computational and Structural Biotechnology Journal, 2025
SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion.
Canson DM et al.
Bioinformatics (oxford, England), 2023
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
Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer.
Keskin Karakoyun H et al.
Frontiers in Genetics, 2023
Clinical, splicing, and functional analysis to classify BRCA2 exon 3 variants: Application of a points-based ACMG/AMP approach.
Thomassen M et al.
Human Mutation, 2022
A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants.
Qorri E et al.
International Journal of Molecular Sciences, 2022
Breast cancer risks associated with missense variants in breast cancer susceptibility genes.
Dorling L et al.
Genome Medicine, 2022
Variomes: a high recall search engine to support the curation of genomic variants.
Pasche E et al.
Bioinformatics (oxford, England), 2022
Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability.
Reza MN et al.
Scientific Reports, 2021
Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays.
Khoruddin NA et al.
Scientific Reports, 2021
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
Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2: In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants.
Özkan S et al.
International Journal of Molecular Sciences, 2021
Predicting the Disease Risk of Protein Mutation Sequences With Pre-training Model.
Li K et al.
Frontiers in Genetics, 2020
REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification.
Tian Y et al.
Scientific Reports, 2019
Germline variation in BRCA1/2 is highly ethnic-specific: Evidence from over 30,000 Chinese hereditary breast and ovarian cancer patients.
Bhaskaran SP et al.
International Journal of Cancer, 2019
A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity.
Shamsani J et al.
Bioinformatics (oxford, England), 2019
Insights into BRCA Cancer Predisposition from Integrated Germline and Somatic Analyses in 7632 Cancers.
Yost S et al.
Jnci Cancer Spectrum, 2019
Finding driver mutations in cancer: Elucidating the role of background mutational processes.
Brown AL et al.
Plos Computational Biology, 2019
Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics.
Ernst C et al.
Bmc Medical Genomics, 2018
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations.
Martelotto LG et al.
Genome Biology, 2014
A guide for functional analysis of BRCA1 variants of uncertain significance.
Millot GA et al.
Human Mutation, 2012
A review of a multifactorial probability-based model for classification of BRCA1 and BRCA2 variants of uncertain significance (VUS).
Lindor NM et al.
Human Mutation, 2012
Impact of BRCA1 and BRCA2 variants on splicing: clues from an allelic imbalance study.
Caux-Moncoutier V et al.
European Journal of Human Genetics : Ejhg, 2009
Application of embryonic lethal or other obvious phenotypes to characterize the clinical significance of genetic variants found in trans with known deleterious mutations.
Judkins T et al.
Cancer Research, 2005