Kaiqiao Li

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

The paper proposes a weighted sparse network learning method for high-dimensional genomics data to improve prediction accuracy and model interpretability, showing robustness and good performance in simulations and a melanoma gene expression case study.

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.

This paper introduces a dual form of the Cox partial likelihood loss using Fenchel duality, enabling efficient kernel learning for survival analysis with censored outcomes.

This study shows that schizophrenia polygenic risk score predicts negative symptoms, cognition, and diagnostic shifts in psychotic disorders over 20 years.

This paper introduces an R package for multiple kernel learning, showing improved classification by combining multiple data sources in ovarian cancer.