Applications to Genetics and Health Data
Genetic and clinical studies must often combine heterogeneous data while accommodating imperfect instruments, population differences, and limits on patient-level data sharing. My work brings robust causal inference, federated learning, and transfer learning to these problems, with the aim of producing biomarker findings and risk predictions that remain reproducible across biobanks, hospitals, and patient populations.
Related work 3
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Xiong, X., Guo, Z., Zhu, H., Hong, C., Smoller, J. W., Cai, T., Liu, M✉. (2026).
Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI.
Technical Report
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Yao, M., Miller, G., Vardarajan, B., Baccarelli, A., Guo, Z.✉, and Liu, Z.✉ (2024).
Deciphering proteins in Alzheimer’s disease: A new Mendelian randomization method integrated with AlphaFold3 for 3D structure prediction.
Cell Genomics, 4(12), 100700.
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Wang, X., Zhou, H., · · ·, 4CE, Avillach, P.✉, Guo, Z.✉, and Cai, T.✉ (2022).
Surv-Maximin: Robust Federated Approach to Transporting Survival Risk Prediction Models.
Journal of Biomedical Informatics, 134 (2022): 104-176.