Sándor Szedmák

This paper develops an improved machine learning model for predicting drug combination dose-response surfaces, showing better performance than scalar-valued synergy score methods.

This paper introduces comboKR, a structured prediction method for drug combination response surfaces, showing improved performance in predicting responses for new drugs.

This paper uses multi-agent reinforcement learning to optimize microbial strain production, showing improved convergence and stability in synthetic biology applications.

The paper introduces comboLTR, a method for predicting drug combination effects using latent tensor reconstruction, outperforming existing methods in speed and accuracy.

comboFM predicts drug combination effects using machine learning, achieving high accuracy in cancer cell line screening and validating novel synergies.