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

Applied Mixed Models BIOST2086 Spring 2013, Spring 2014, Spring 2016, 2017
Biostatistics Seminar  BIOST2025 Spring 2014, Fall 2014

1. Funding Agency: NIH/NEI
   Grant Number: R21EY030488
   Grant Title: Deep-learning-based prediction of AMD and its progression with GWAS and fundus image data
   Role on Grant: Principal Investigator
   Years Inclusive: 8/1/2020 – 5/31/2022
   Total Direct Costs: $270,000
2. Funding Agency: NIH/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: 1/1/2019 – 12/31/2019
   Total Direct Costs: $10,000                  
3. 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
4. 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 

2021 James L. Craig Excellence in Education Award
https://publichealth.pitt.edu/news/details/articleid/8966/ying-wins-2021-craig-award

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

2021:

• Yue W, Hsu JC, Chen W, Chew EY, Ding Y^*. (2021) Identification and Inference for Subgroups with Differential Treatment Efficacy from Randomized Controlled Trials with Survival Outcomes through Multiple Testing. (The earlier version won the Best Poster Award in ASA Pittsburgh Chapter 2019 Meeting.) https://arxiv.org/abs/2003.10528 Statistics in Medicine. Accepted
• Yue W, Wang X, Chew EY, Ding Y^*. (2021) Confident Identification of Subgroups from SNP Testing in RCTs with Binary Outcomes. Biometrical Journal. https://doi.org/10.1002/bimj.202000170
• Yan Q, Jiang Y, Huang H, Xin H, Swaroop A, Chew EY, Weeks DE, Chen W*, Ding Y*. (2021) GWAS-based Machine Learning for Prediction of Age-Related Macular Degeneration Risk. Translational Vision Science & Technology (TVST). https://doi.org/10.1167/tvst.10.2.29
• Cui X, Dickhaus T, Ding Y, Hsu JC. Handbook of Multiple Comparisons. Chapman & Hall/CRC, 2021 (Preview: Handbook of Multiple Comparisons (uni-bremen.de))
• Ding Y^*, Wei Y, Wang X, Hsu JC. Testing SNPs in Targeted Drug Development. Book Chapter In: Cui X, Dickhaus T, Ding Y, Hsu JC. Handbook of Multiple Comparisons. Chapman & Hall/CRC, 2021

2020:

• Sun T, Wei Y, Chen W, Ding Y^*. (2020) Genome-wide Association Study-based Deep Learning for Survival Prediction. (The earlier version won the 2019 LiDS Conference Student Poster Award.) Statistics in Medicine. https://doi.org/10.1002/sim.8743.
• Chen L-W, Cheng Y, Ding Y, Li R. (2020) Quantile Association Regression on Bivariate Survival Data. Canadian Journal of Statistics. doi/10.1002/cjs.11577.
• ­Wang X⁺, Sun Z⁺, Zhang Y, Xu Z, Huang H, Duerr R, Chen K, Ding Y^*, Chen W^*. (2020) BREM-SC: A Bayesian Random Effects Mixture Model for Joint Clustering Single Cell Multi-omics Data. (The paper won the 2020 ICSA Student Paper Award.) Nucleic Acid Research. 48(11): 5814–5824 doi: 10.1093/nar/gkaa314. PMID: 32379315.
• Sun T, Ding Y^*. (2020) CopulaCenR: Copula based Regression Models for Bivariate Censored Data in R. The R Journal. https://doi.org/10.32614/RJ-2020-025.
• Yan Q, Weeks DE, Xin H, Huang H, Swaroop A, Chew EY, Ding Y^*, Chen W^*. (2020) Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression. Nature Machine Intelligence. 2(2):141-150 DOI: 10.1038/s42256-020-0154-9 PMID: 32285025.
• Ding Y^*, Wei Y, Wang X. Logical Inference on Treatment Efficacy When Subgroups Exist. Book Chapter In: Ting N, Cappelleri JC, Ho S, Chen DG. Design and Analysis of Subgroups with Biopharmaceutical Applications. New York: Springer, 2020. 

2019:

• Wei Y⁺, Liu Y⁺, Sun T, Chen W, Ding Y^*. (2019) Gene-based Association Analysis for Bivariate Time-to-event Data through Functional Regression with Copula Models. (The earlier version won the 2019 LiDS Conference Student Paper Award.) Biometrics. DOI:10.1111/biom.13165
• Sun T, Ding Y^*. (2019) Copula-based semiparametric transformation model for bivariate data under general interval censoring. (The earlier version won the 2019 ENAR Distinguished Student Paper Award.) Biostatistics. DOI: 10.1093/biostatistics/kxz032
• Sun Z, Chen L, Xin H, Huang Q, Cillo AR, Tabib T, Kolls JK, Bruno TC, Lafyatis R, Vignali DAA, Chen K, Ding Y*, Hu M*, Chen W*. (2019) BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies. (The earlier version won the 2019 ENAR Distinguished Student Paper Award.) Nature Communication. 10(1):1649 Doi: 10.1038/s41467-019-09639-3. PMID: 30967541
• Sun T⁺, Liu Y⁺, Cook RJ, Chen W, Ding Y^*. (2019). Copula-based Score Test for Bivariate Time-to-event Data, with Application to a Genetic Study of AMD Progression. (The earlier version won the Best Poster Award in ASA Pittsburgh Chapter 2017 Meeting.) Lifetime Data Analysis. DOI: 10.1007/s10985-018-09459-5. PMID: 30560439
• Lin HM, Xu H, Ding Y, Hsu JC. (2019). Correct and Logical Inference on Efficacy in Subgroups and Their Mixture for Binary Outcomes. Biometrical Journal. 61(2): 8-26. PMID: 30353566

2018:

• 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.
• 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
• 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
• 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

2017 and before:

• 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 
• 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
• 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
• 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
• 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
• 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.
• Ding Y, Fu H. (2013). Bayesian Indirect and Mixed Treatment Comparisons Across Longitudinal Time Points. Statistics in Medicine 32 (15):2613-28. PMID: 23229717
• Banerjee M, Ding Y, Noone A. (2012). Identifying Representative Trees from Ensembles. Statistics in Medicine 31(15):1601-16.  PMID: 22302520
• 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
• 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/?sortby=pubDate&sdirection=descending