Hong‐Gee Kim

COOBoostR uses extreme gradient boosting to predict tumor tissue or cell-of-origin using chromatin marks and mutation density data, outperforming random forest methods in speed and accuracy.

The paper uses whole-genome sequencing to predict cell-of-origin for primary liver cancers, finding heterogeneity in origin predictions across cancer types.

BEE provides a natural language query interface for exploring relationships among biomedical entities across multiple datasets.

The paper predicts cancer cell-of-origin from DNA sequences by matching mutational landscapes to chromatin modification patterns in normal tissues.