Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis. - Zeyu Zhang; Yuanshen Zhao; Jingxian Duan; Yaou Liu; Hairong Zheng; Dong Liang 001; Zhenyu Zhang; Zhi-Cheng Li 001 (CoRR, 2024)

MRI and RNA-seq fusion for prediction of pathological response to neoadjuvant chemotherapy in breast cancer. - Hui Li; Yuanshen Zhao; Jingxian Duan; Jia Gu; Zaiyi Liu; Huailing Zhang; Yuqin Zhang; Zhi-Cheng Li 001 (Displays, 2024)

Identifying pathological groups from MRI in prostate cancer using graph representation learning. - Feng Liu; Yuanshen Zhao; Chongzhe Yan; Jingxian Duan; Lei Tang; Bo Gao; Rongpin Wang (Displays, 2024)

Drop and Align: Fusing Pathology and Genomics via Graph Learning for Cancer Survival Prediction. - Zeyu Zhang; Yuanshen Zhao; Jingxian Duan; Yaou Liu; Dong Liang 001; Zhi-Cheng Li 001 (ISBI, 2025)

SurvGraph: A hybrid-graph attention network for survival prediction using whole slide pathological images in gastric cancer. - Yuanshen Zhao; Longsong Li; Xi Yu; Ke Han; Jingxian Duan; Dong Liang 001; Ningli Chai; Zhicheng Li 001 (Neural Networks, 2025)