Our research is broadly aiming to develop novel statistical and machine learning methods for personalized medicine. Recently we are focusing on integrative omics and innovative clinical trial designs, which are two areas lying on the two ends of the research pathway. In particular, we want to idenfity biological networks across multiple omic layers that drive the disease progression at detailed molecular level. Based on actionable biomarker networks, further clinical trials may be designed to develop novel treatments for the disease.
Hu B, Li L, Wang XF, Greene T. Nonparametric multi-state representations of survival and longitudinal data with measurement error. Statistics in Medicine. 2012, 31:2303-17.
Hu B, Gadegbeku, C, Lipkowitz, M, Rostand S, Lewis J, Wright JT, Appel L, Greene T, Gassman J, Astor B. Kidney function can improve in patients with hypertensive chronic kidney disease. Journal of the American Society of Nephrology. 2012 Apr; 23(4): 706-713.
Xu Y, Hu B, Choi AJ, Gopalan B, Lee BH, Kalady MF, Church JM, Ting AH. Unique DNA methylome profiles in CpG island methylator phenotype colon cancers. Genome Research. 2012, 22:283-291.
Hu B, Fu Z. Predicting utility for joint health states: a general framework and a new nonparametric estimator. Medical Decision Making. 2010, 30: E29-E39.
Li L, Hu B, Greene T. A semiparametric joint model for longitudinal and survival data with application to Hemodialysis study. Biometrics. 2009, 65: 737-745.
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