Kasthuri S. Kannan

The paper introduces EGNF, a graph-based framework for biomarker discovery that outperforms traditional methods in classification accuracy and interpretability across cancer datasets.

This paper develops network-regularized AFT models with inverse-degree penalties for survival analysis in cancer genomics, showing improved biological interpretability and reduced false positives.