Ying Ding, PhD

My primary research interests include semiparametric methods and inferences, especially for time-to-event data; subgroup analysis such as simultaneous inference and biomarker/subgroup identification. Currently, my collaborative research focuses on proteomic experiment design, network analysis for psychiatric disorders and progression analysis of AMD (Age-related Macular Degeneration).

Ph.D. (2010) Department of Biostatistics, University of Michigan, MI
M.A. (2005) Department of Mathematics, Indiana University Bloomington, IN
B.S. (2003) Department of Mathematics, Nanjing University, China

1. Funding Agency: UPMC 
   Grant Title: Competitive Medical Research Fund
   Role on Grant:: Principal Investigator
   Years Inclusive: 7/1/2015 - 12/31/2017
   Total Direct Costs: $25,000                                                                  
2. Funding Agency: NIH/NIMH
   Grant Number: R03MH108849
   Grant Title: Novel and Robust Methods for Differential Protein Network Analysis of Proteomics Data in Schizophrenia Research
   Role on Grant: Principal Investigator
   Years Inclusive: 7/1/2016 – 6/30/2018
   Total Direct Costs: $100,000
3. Funding Agency: Clinical and Translational Science Institute, University of Pittsburgh
   Grant Title: Deep Learning with GWAS to Predict AMD Progression
   Role on Grant: Principal Investigator
   Years Inclusive: 2/1/2019 – 1/31/2020
   Total Direct Costs: $10,000

*: corresponding/senior author; +: co-first author; _: student advisee 

1. Sun T, Liu Y, Cook RJ, Chen W, Ding Y*. (2018). Copula-based Score Test for Bivariate Time-to-event Data, with Application to a Genetic Study of AMD Progression. Lifetime Data Analysis. In Press.
2. Lin HM, Xu H, Ding Y, Hsu JC. (2018). Correct and Logical Inference on Efficacy in Subgroups and Their Mixture for Binary Outcomes. Biometrical Journal. DOI: 10.1002/bimj.201800002. PMID: 30353566
3. Ding Y*, Li GY, Liu Y, Ruberg SJ, Hsu JC. (2018). Confident Inference For SNP Effects On Treatment Efficacy. Annals of Applied Statistics. 12(3): 1727-1748.
4. Ding Y^*,+, Kong S⁺, Kang S, Chen W. (2018). A Semiparametric Imputation Approach for Regression with Censored Covariate, with Application to an AMD Progression Study. Statistics in Medicine. 37: 3293–3308. PMID: 29845616
5. Yan Q, Ding Y⁺, Liu Y, Sun T, Fritsche LG, Clemons T, Ratnapriya R, Klein ML, Cook RJ, Liu Y, Fan R, Wei L, Abecasis GR, Swaroop A, Chew EY, AREDS2 research group, Weeks  DE, Chen W. (2018). Genome-wide Analysis of Disease Progression in Age-related Macular Degeneration. Human Molecular Genetics. 27(5):929-940. PMID: 29346644
6. Sun Z, Wang T, Deng K, Wang X-F, Lafyatis R, Ding Y, Hu M, Chen W. (2018). DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data. Bioinformatics. 34(1): 139-146. PMID: 29036318
7. Ding Y, Liu Y, Yan Q, Fritsche LG, Cook RJ, Clemons T, Ratnapriya R, Klein ML, Abecasis GR, Swaroop A, Chew EY, Weeks DE, Chen W. (2017). Bivariate Analysis of Age-Related Macular Degeneration Progression Using Genetic Risk Scores. Genetics. 206(1):119-133. PMID: 28341650 (Received editorial highlights and media reports).
8. Ding Y*, Lin HM. Data Analysis of in vivo Fluorescence Imaging Studies. In: Bai M, editors. In Vivo Fluorescence Imaging: Methods and Protocols. New York: Springer, 2016.
9. Wang T, Ren Z, Ding Y, Zhou F, Sun Z, MacDonald ML, Sweet RA, Chen W. (2016). FastGGM: An efficient algorithm for the inference of Gaussian graphical model in biological networks. PLoS Computational Biology. 12(2): e1004755. PMID: 26872036
10. Fan R, Wang Y, Yan Q, Ding Y, Weeks DE, Lu Z, Ren H, Cook R J, Xiong M, Swaroop A, Chew E Y, and Chen W. (2016). Gene-based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions. Genetic Epidemiology. 40(2): 133-43. PMID: 26782979
11. Ding Y*, Lin HM, Hsu JC. (2016). Subgroup Mixable Inference on Treatment Efficacy in Mixture Populations, with an Application to Time-to-Event Outcomes. Statistics in Medicine. 35(10):1580-94. PMID: 26646305
12. Ding Y*, Nan B. (2015). Estimating Mean Survival Time: When is it Possible? Scandinavian Journal of Statistics 42(2):397-413. PMID: 26019387 PMCID: PMC4442028
13. Shen L, Ding Y, Battioui C. A Framework of Statistical Methods for Identification of Subgroups with Differential Treatment Effects in Randomized Trials. (2015) In: Chen Z, Liu A, Qu Y, Tang L, Ting N & Tsong Y, eds. Applied Statistics in Biomedicine and Clinical Trials Design: Selected Papers from 2013 ICSA/ISBS Joint Statistical Meetings. New York: Springer.
14. Ding Y*, Fu H. (2013). Bayesian Indirect and Mixed Treatment Comparisons Across Longitudinal Time Points. Statistics in Medicine 32 (15):2613-28. PMID: 23229717
15. Banerjee M, Ding Y, Noone A. (2012). Identifying Representative Trees from Ensembles. Statistics in Medicine 31(15):1601-16.  PMID: 22302520
16. Ding Y, Nan B. (2011). 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 39(6): 3032-3061. PMID: 24436500  PMCID:  PMC3890689
17. Ding Y, Choi H, Nesvizhskii AI. (2008). Adaptive Discriminant Function Analysis and Reranking of MS/MS Database Search Results for Improved Peptide Identification in Shotgun Proteomics. Journal of Proteome Research 7(11): 4878-89.  PMID: 18788775  PMCID: PMC3744223

Complete List of Published Work in My Bibliography:
http://www.ncbi.nlm.nih.gov/sites/myncbi/1f510URSbxjQh/bibliography/47222780/public/?sort=date&direction=ascending