Xuejun Li

The paper introduces Mime, an R package for building and visualizing machine learning models to predict clinical outcomes and select features using transcriptional data.

The paper develops a machine learning framework to identify RCD-related genes in glioma, identifies SLC43A3 as an oncogenic biomarker, and proposes a gene pair signature for prognosis prediction.

TCCIA integrates circRNA profiles, immunotherapy response data, and clinical outcomes to explore circRNA roles in cancer immunotherapy.

IOBR is a computational tool that integrates multi-omics data to analyze tumor microenvironment and signatures, enabling batch analysis and comparison of immunotherapy-related features.