Etai Jacob

The paper presents a machine learning framework to classify variants in cell-free DNA as clonal hematopoiesis or tumor origin, achieving better classification rates than existing methods.

The paper integrates knowledge graphs into machine learning models to improve survival prediction and biomarker discovery in non-small-cell lung cancer patients, showing significant improvements in hazard ratios and overall survival differentiation.

This study develops a multimodal fusion pipeline that improves survival prediction in cancer patients by outperforming unimodal models using The Cancer Genome Atlas data.