Publications

Selected work, most recent first. Lab members' names are in bold. Manuscripts that are submitted, under review, or in preparation are not listed here. For the complete and current record, see Google Scholar or the NCBI bibliography.

2026

• Zeng L, Tang W, Ren Z, Ding Y. Mini-batch estimation for deep Cox models: statistical foundations and practical guidance. Journal of the American Statistical Association. 2026.
• Bo N, Ding Y. Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes. Lifetime Data Analysis. 2026;32(1):11.
• Jiang C, Krivinko J, Yu Z, Sweet RA, Zeng L, Wang H, Ding Y, Zeng Z, et al. Comparative mortality risk of aripiprazole, olanzapine, quetiapine, and risperidone in Alzheimer's disease: a real-world retrospective cohort study. CNS Drugs. 2026;40(6):823–833.

2025

• Zeng L, Zhang J, Chen W, Ding Y. tdCoxSNN: time-dependent Cox survival neural network for continuous-time dynamic prediction. Journal of the Royal Statistical Society Series C: Applied Statistics. 2025;74(1).
• Sui Z, Ding Y, Tang L. Robust transfer learning for individualized treatment rules in the presence of missing data. Biostatistics. 2025;26(1):kxaf023.
• Bo N, Jeong J-H, Forno E, Ding Y. Evaluating meta-learners to analyze treatment heterogeneity in survival data: application to electronic health records of pediatric asthma care in the COVID-19 pandemic. Statistics in Medicine. 2025;44(3–4):e10333.
• Liu K, Zu Y, Yi D, Ding Y, Sun T. Neural network-based dynamic prediction for interval-censored data with time-varying covariates: application to Alzheimer's disease. Statistics in Medicine. 2025;44(15–17):e70190.
• Zhang J, Zhao C, Zeng L, Huang H, Ding Y, Chen W. TV-LSTM: multimodal deep learning for predicting the progression of late age-related macular degeneration using longitudinal fundus images and genetic data. AI Sensors. 2025;1(1):6.
• Swaminathan G, Lin Y-C, Ni J, Khalid A, Tsai C-Y, Ding Y, Bo N, et al. Why is the rectal route for NSAIDs favorable for preventing post-ERCP pancreatitis? Pancreatology. 2025;25(4):491–498.

2024

• Chen L, Wang Y, Cai C, Ding Y, Kim RS, Lipchik C, Gavin PG, Yothers G, et al. Machine learning predicts oxaliplatin benefit in early colon cancer. Journal of Clinical Oncology. 2024;42(13):1520–1530.
• Bo N, Wei Y, Zeng L, Kang C, Ding Y. A meta-learner framework to estimate individualized treatment effects for survival outcomes. Journal of Data Science. 2024;22(4):505.
• Zhou X, Cai M, Yue M, Celedón JC, Wang J, Ding Y, Chen W, Li Y. Molecular group and correlation guided structural learning for multi-phenotype prediction. Briefings in Bioinformatics. 2024;25(6):bbae585.
• Sun T, Liang W, Zhang G, Yi D, Ding Y, Zhang L. Penalised semi-parametric copula method for semi-competing risks data: application to hip fracture in elderly. Journal of the Royal Statistical Society Series C: Applied Statistics. 2024;73(1).
• Liu J, Bo N, Zhou X, Forno E, Ding Y. Predicting pediatric asthma severe outcomes using machine learning methods for EHR data with repeated clinic visits. Journal of Statistical Research. 2024;58(1):131–149.

2023

• Sun T, Ding Y. Neural network on interval-censored data with application to the prediction of Alzheimer's disease. Biometrics. 2023;79(3):2677–2690.
• Sun T, Cheng Y, Ding Y. An information ratio-based goodness-of-fit test for copula models on censored data. Biometrics. 2023;79(3):1713–1725.
• Sun T, Li Y, Xiao Z, Ding Y, Wang X. Semiparametric copula method for semi-competing risks data subject to interval censoring and left truncation: application to disability in elderly. Statistical Methods in Medical Research. 2023;32(4):656–670.
• Zhou X, Zhang J, Ding Y, Huang H, Li Y, Chen W. Predicting late-stage age-related macular degeneration by integrating marginally weak SNPs in GWA studies. Frontiers in Genetics. 2023;14:1075824.
• Fan P, Zeng L, Ding Y, Kofler J, Silverstein J, Krivinko J, Sweet RA, et al. Combination of antidepressants and antipsychotics as a novel treatment option for psychosis in Alzheimer's disease. CPT: Pharmacometrics & Systems Pharmacology. 2023;12(8):1119–1131.
• Rahman MA, Cai C, Bo N, McNamara DM, Ding Y, Cooper GF, Lu X, Liu J. An individualized Bayesian method for estimating genomic variants of hypertension. BMC Genomics. 2023;23(Suppl 5):863.
• Gomez Marti JL, Nasrazadani A, Ding Y, Normolle D, Brufsky AM. Twenty-year follow-up of a phase II trial of taxotere/carboplatin/herceptin in patients with metastatic HER2-positive breast cancer. The Oncologist. 2023;28(11):e1123–e1126.
• Ni J, Khalid A, Lin Y-C, Barakat MT, Wang J, Tsai C-Y, Azar PRS, Ding Y, et al. Preclinical safety evaluation of calcineurin inhibitors delivered through an intraductal route to prevent post-ERCP pancreatitis. Pancreatology. 2023;23(4):333–340.

2022

• Ganjdanesh A, Zhang Z, Chew EY, Ding Y, Chen W, Huang H. LONGL-Net: a temporal correlation structure-guided deep learning framework for predicting longitudinal age-related macular degeneration severity. PNAS Nexus. 2022;1(1):pgab003.
• Wang X, Xu Z, Zhou X, Zhang Y, Huang H, Ding Y, Duerr RH, Chen W. SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics. PNAS Nexus. 2022.
• Fan P, DeChellis-Marks MR, Ding Y, Kofler J, Sweet RA, Wang L. Efficacy difference of antipsychotics in Alzheimer's disease and schizophrenia: explained with network efficiency and pathway analysis methods. Briefings in Bioinformatics. 2022.

2021

• Wei Y, Hsu JC, Chen W, Chew EY, Ding Y. Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing. Statistics in Medicine. 2021.
• Sun T, Ding Y. Copula-based semiparametric transformation model for bivariate data under general interval censoring. Biostatistics. 2021;22(2):315–330.
• Wei Y, Wang X, Chew EY, Ding Y. Confident identification of subgroups from SNP testing in RCTs with binary outcomes. Biometrical Journal. 2021.
• Yan Q, Jiang Y, Huang H, Xin H, Swaroop A, Chew EY, Weeks DE, Chen W, Ding Y. GWAS-based machine learning for prediction of age-related macular degeneration risk. Translational Vision Science & Technology. 2021;10(2):29.

2020

• Yan Q, Weeks DE, Xin H, Huang H, Swaroop A, Chew EY, Ding Y, Chen W. Deep-learning-based prediction of late age-related macular degeneration progression. Nature Machine Intelligence. 2020;2(2):141–150.
• Sun T, Wei Y, Chen W, Ding Y. Genome-wide association study-based deep learning for survival prediction. Statistics in Medicine. 2020;39(30):4605–4620.
• Wang X, Sun Z, Zhang Y, Xu Z, Huang H, Duerr R, Chen K, Ding Y, Chen W. BREM-SC: a Bayesian random effects mixture model for joint clustering single cell multi-omics data. Nucleic Acids Research. 2020.
• Sun T, Ding Y. CopulaCenR: copula based regression models for bivariate censored data in R. The R Journal. 2020.

Selected earlier work

• Sun Z, Chen L, Xin H, Huang Q, Cillo AR, Tabib T, et al., Ding Y, Hu M, Chen W. A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies. Nature Communications. 2019;10(1):1649.
• Wei Y, Liu Y, Sun T, Chen W, Ding Y. Gene-based association analysis for bivariate time-to-event data through functional regression with copula models. Biometrics. 2019;76:619–629.
• Ding Y, Li GY, Liu Y, Ruberg SJ, Hsu JC. Confident inference for SNP effects on treatment efficacy. Annals of Applied Statistics. 2018;12(3):1727–1748.
• Ding Y, Liu Y, Yan Q, Fritsche LG, Cook RJ, Clemons T, et al., Weeks DE, Chen W. Bivariate analysis of age-related macular degeneration progression using genetic risk scores. Genetics. 2017;206(1):119–133.
• Ding Y, Nan B. A sieve M-theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data. Annals of Statistics. 2011;39(6):3032–3061.