Halil İbrahim Kuru

DeepSynBa predicts complete dose-response matrices for drug combinations, outperforming existing methods and enabling more precise and actionable drug synergy analysis.

This paper shows that using one-hot encodings for drugs and cell lines in synergy prediction models can perform as well as or better than using molecular features, suggesting models rely more on identifiers than features.

This paper introduces PDSP, a deep learning framework that uses patient-specific single-drug response data to predict personalized drug synergy, improving accuracy by 27% in leukemia patients.

MatchMaker predicts drug synergy using chemical structures and gene expression, improving performance over existing methods on a large drug combination dataset.

PRER improves survival prediction in 9/10 cancers by integrating protein expression with PPI networks using pairwise relative expression features.