Olga Kondrashova

This study identifies genomic mechanisms enabling BRCA1 methylation loss and promoter bypass in ovarian cancer, leading to PARP inhibitor resistance.

The paper develops a multi-omic random forest model to predict immunotherapy response in melanoma using SHAP for feature threshold identification.

The paper develops machine learning models to predict pathogenicity of BRCA1 and BRCA2 variants using cancer genomic profiles, improving classification of variants of uncertain significance.

This study shows that DNMT1 inhibitors reduce BRCA1 promoter methylation in HGSOC, leading to BRCA1 re-expression and PARP inhibitor resistance.

The paper generates a homozygous BRCA1-methylated OVCAR8 cell line using CRISPR editing to study meBRCA1 stability and PARPi resistance.

This paper benchmarks Bayesian deep learning models for predicting cancer of unknown primary, showing improved uncertainty generalisation and accuracy under distributional shifts.

The paper introduces SAI-10k-calc, a tool that extends SpliceAI to predict splicing aberrations and their effects, achieving high sensitivity and specificity for variant impact prediction.

The paper develops endometrial cancer PDX models that capture tumor heterogeneity and predict response to talazoparib.

This paper uses CRISPR/Cas9 to edit the acetylcholinesterase gene in Schistosoma mansoni eggs, resulting in reduced AChE activity and decreased granuloma size in mice.