Vessela Nedelcheva Kristensen

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 study identifies common dysregulated transcriptional networks across cancers linked to loss of enhancer methylation through pan-cancer emQTL analysis.

This study examines differences in polygenic risk score distributions across European populations and shows how country-specific calibration can improve breast cancer risk prediction.

The paper uses scRNA-seq and explainable machine learning to analyze how tumor microenvironment cell types affect neoadjuvant chemotherapy outcomes in breast cancer.

The paper introduces a deep learning model with attention mechanisms to predict DNA methylation from gene expression and sequence data, achieving high correlation and identifying potential methylation-related genes.