Chang In Moon

This paper develops chemotherapy-specific predictors for triple-negative breast cancer using patient-derived xenografts and multi-omic data, achieving high AUROC scores for response prediction.

This study develops a deep auxiliary learning approach to predict TNBC treatment response using clinical trial data, showing improved performance over traditional methods.

The paper analyzes how EBNA2 and EBNA3 vTFs bind the human genome across cell-types, showing they use both cell-type specific and independent cofactors and reveal novel sequence motifs.

The paper uses statistical and machine learning methods to analyze NF1 tumor genomic data, identifying key signaling pathways and molecular features associated with different tumor types and potential drug targets.