Eitan Rubin

OptiRanker simulates and compares drug prioritization algorithms to rank their predictive accuracy, enabling efficient in vivo trial design for personalized medicine.

TCUP predicts tissue of origin and cancer of unknown primary using ensemble learning with embeddings and ablation analysis, achieving high accuracy and providing gene importance insights.

The paper reanalyzes a breast cancer compendium to identify HNF4A as a key gene associated with recurrence-free survival, validated in independent cohorts.

The paper uses binary classification machine learning to identify sex-dependent mutation signatures in melanoma, finding genes like LAMA2 and TPTE that predict patient sex and survival.