Associated Papers
VarMeter2: An enhanced structure-based method for predicting pathogenic missense variants through Mahalanobis distance.
Ohno S et al.
Computational and Structural Biotechnology Journal, 2025
Evolutionary origin of germline pathogenic variants in human DNA mismatch repair genes.
Lei H et al.
Human Genomics, 2024
Classification of MLH1 Missense VUS Using Protein Structure-Based Deep Learning-Ramachandran Plot-Molecular Dynamics Simulations Method.
Tam B et al.
International Journal of Molecular Sciences, 2024
Comparison of immediate germline sequencing and multi-step screening for Lynch syndrome detection in high-risk endometrial and colorectal cancer patients.
Chao AS et al.
Journal of Gynecologic Oncology, 2024
SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion.
Canson DM et al.
Bioinformatics (oxford, England), 2023
Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification.
Tam B et al.
Iscience, 2023
Prevalence and spectrum of DNA mismatch repair gene variation in the general Chinese population.
Zhang L et al.
Journal of Medical Genetics, 2022
Characteristics of Germline Non-BRCA Mutation Status of High-Risk Breast Cancer Patients in China and Correlation with High-Risk Factors and Multigene Testing Suggestions.
Su Y et al.
Frontiers in Genetics, 2021
Contribution of mRNA Splicing to Mismatch Repair Gene Sequence Variant Interpretation.
Thompson BA et al.
Frontiers in Genetics, 2020
Estimating dispensable content in the human interactome.
Ghadie M et al.
Nature Communications, 2019
A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity.
Shamsani J et al.
Bioinformatics (oxford, England), 2019
Using Somatic Mutations from Tumors to Classify Variants in Mismatch Repair Genes.
Shirts BH et al.
American Journal of Human Genetics, 2018
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
Computational Tools for Splicing Defect Prediction in Breast/Ovarian Cancer Genes: How Efficient Are They at Predicting RNA Alterations?
Moles-Fernández A et al.
Frontiers in Genetics, 2018
MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing.
Mort M et al.
Genome Biology, 2014
Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis.
Caminsky N et al.
F1000research, 2014
Missense mutations of MLH1 and MSH2 genes detected in patients with gastrointestinal cancer are associated with exonic splicing enhancers and silencers.
Zhu M et al.
Oncology Letters, 2013
Human Splicing Finder: an online bioinformatics tool to predict splicing signals.
Desmet FO et al.
Nucleic Acids Research, 2009
Germ line MLH1 and MSH2 mutations in Taiwanese Lynch syndrome families: characterization of a founder genomic mutation in the MLH1 gene.
Tang R et al.
Clinical Genetics, 2009
A large fraction of unclassified variants of the mismatch repair genes MLH1 and MSH2 is associated with splicing defects.
Tournier I et al.
Human Mutation, 2008
Aberrant splicing in MLH1 and MSH2 due to exonic and intronic variants.
Pagenstecher C et al.
Human Genetics, 2006
The use of microsatellite instability, immunohistochemistry and other variables in determining the clinical significance of MLH1 and MSH2 unclassified variants in Lynch syndrome.
Lucci-Cordisco E et al.
Cancer Biomarkers : Section a of Disease Markers, 2006
Spectrum and frequencies of mutations in MSH2 and MLH1 identified in 1,721 German families suspected of hereditary nonpolyposis colorectal cancer.
Mangold E et al.
International Journal of Cancer, 2005
Cancer risk in 348 French MSH2 or MLH1 gene carriers.
Parc Y et al.
Journal of Medical Genetics, 2003