Xinran Dong

The paper introduces SINBA, a computational framework that identifies synergistic, brain-penetrant drug combinations for high-risk medulloblastoma, validating several candidates in preclinical models.

This study introduces a deep learning framework, FIND, to better classify genetic variants by their deleteriousness and trait-modulating effects, showing improved performance over existing methods.

The paper introduces HapICE, a method to improve variant detection in SBDS using NGS, achieving higher diagnostic accuracy than conventional methods.

The paper identifies TCF4 and ID3 as key transcription factors driving mammary epithelial stem cell differentiation through single-cell RNA sequencing analysis.