Vasileios Stathias

The paper integrates single-cell RNA sequencing data with drug response signatures to identify drug-sensitive and resistant glioblastoma cell states and predict synergistic drug combinations.

This study identifies BET inhibition as a promising therapeutic strategy for uveal melanoma by demonstrating its efficacy in reducing metastasis and improving survival in a mouse model.

This paper develops a deep learning model to predict drug sensitivity in cancer using gene expression data and perturbation signatures, validated in prostate cancer cell lines.

iLINCS provides a web-based platform for analyzing omics signatures of diseases and cellular perturbations through integrative analytics and visualization tools.

The paper develops a scoring system to prioritize understudied kinases as potential cancer targets using TCGA data.

The paper introduces the Drug Target Ontology (DTO) to classify and integrate drug discovery data by formalizing druggable protein targets and their characteristics.