Khoa Anh Tran

This paper shows that inhibiting BRN2 in melanoma cells reverses anoikis resistance and increases sensitivity to Vemurafenib.

The paper develops a multi-omic random forest model to predict immunotherapy response in melanoma using SHAP for feature threshold identification.

The paper develops machine learning models to predict pathogenicity of BRCA1 and BRCA2 variants using cancer genomic profiles, improving classification of variants of uncertain significance.

The paper identifies six metabolic subgroups of prostate cancer using transcriptomics data, showing distinct survival outcomes and highlighting metabolic pathways as potential early diagnostic indicators.