John Zobolas

TRAFIKK predicts and explains anticancer drug synergies by simulating drug effects in cell-specific models, achieving high recall and revealing both conserved and context-dependent mechanisms.

The paper establishes a statistical framework to identify synergistic anticancer drug interactions using large-scale cell line data and reference null distributions.

BooLEVARD quantifies paths leading to node activation or repression in Boolean models, enhancing signal strength analysis in signaling networks.

The paper predicts drug synergies using a logical modeling framework that combines manual curation with automated parameterization, showing that automated methods can outperform manual models in some cases.

The paper introduces CausalTAB, an updated format for representing causal signaling interactions, enhancing the precision and coverage of signaling network modeling.