Sijie Yao

GPS-Net identifies prognostic pathway modules using network regularized kernel learning to improve biomarker accuracy and reduce computational complexity in cancer immunotherapy studies.

CGPA provides a platform for multi-context gene-centric prognostic analysis, enabling customized exploration of gene relationships and biomarker discovery in cancer research.

SmartImpute improves scRNA-seq imputation by targeting marker genes, enhancing accuracy and efficiency while reducing computational burden.

The paper identifies differentially methylated regions in cfDNA from tumor and normal tissues using paired-sample analysis to discover potential biomarkers for head and neck cancers.

This paper introduces Xsurv, an R package using XGB and LGB for survival analysis, showing it outperforms traditional methods in biomarker discovery for cancer prognosis.