Ksenia Sokolova

HiFIseek identifies gene-specific enrichment of high-impact mutations in genomic regions associated with cancer and breast cancer risk SNPs, showing limited agreement across CRR-score combinations.

The paper generates disease-specific genetic effect maps on the blood transcriptome for five glomerular diseases using whole genome and transcriptome sequencing data from the CureGN cohort.

The paper introduces Mahi, a graph neural network framework that integrates molecular context and functional connectivity to predict gene essentiality and perturbation responses across tissues.

Volaria integrates coding and regulatory variation into gene-centered representations for disease outcome prediction using whole-genome sequencing data.

Otari predicts tissue-specific isoform abundance changes caused by genetic variants using a graph-based attention model trained on genomic and long-read transcriptome data.

Alvessa improves accuracy in functional genomics and drug target assessment by integrating evidence-based reasoning and verification.

This study uses whole-exome sequencing and single-cell DNA sequencing to analyze subclonal evolution in breast cancer during aromatase inhibitor treatment, identifying resistance mechanisms and actionable targets.

This paper introduces cGen, an unsupervised contrastive pre-training method for sequence-based genomic models, showing it improves performance in tasks like chromatin profiling and gene expression prediction.