Maiying Kong, Ph.D.

Education:Maiying Kong, Ph.D. 

B.Sc., Computational Mathematics, Xi’an Jiaotong University, Shaanxi, China; 1990
M.Sc., Computational Mathematics, Xi’an Jiaotong University, Shaanxi, China; 1993
Ph.D., Statistics, Indiana University – Bloomington, Indiana, USA; 2004
Postdoctoral Fellow, Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, Houston, Texas; 2004-2006 

Curriculum Vitae 

Current Positions: 

Professor, Department of Bioinformatics and Biostatistics
Commonwealth Scholar; Commonwealth Institute of Kentucky, University of Louisville
Associate Member, Department of Pediatrics
Member, James Graham Brown Cancer Center 

Contact Information: 

Department of Bioinformatics & Biostatistics
School of Public Health & Information Sciences
University of Louisville
Louisville, KY 40292
Phone:  502/852-3988
Email: 

Research Description: 

One of my current research interests is to study and develop appropriate statistical methods to compare effectiveness of different treatments and procedures based on observational data such as Medicaid data and electronic health record data.  My other current research interest is to study and develop statistical methods for high dimensional data such as mass spectrometry data, and link them with clinical outcomes.  My previous experience and expertise includes longitudinal data analysis, mixed effect models, generalized linear models, smoothing splines, high dimensional data analyses, modeling count data, pre-clinical studies, assessing drug interactions, and assessing drug activity in xenograft models 

Literature Cited: 

  1. Morrissey SM, Geller AE, Hu X, Tieri D, Ding C, Klaes CK, Cooke EA, Woeste MR, Martin ZC, Chen O, Bush SE, Zhang HG, Cavallazzi R, Clifford SP, Chen J, Ghare S, Barve SS, Cai L, Kong M, Rouchka EC, McLeish KR, Uriarte SM, Watson CT, Huang J, Yan J.  A specific low-density neutrophil population correlates with hypercoagulation and disease severity in hospitalized COVID-19 patients.  JCI Insight 2021 May 10;6(9):148435.  doi: 10.1172/jci.insight.148435.  PMID: 33986193.
  2. Saran U, Chandrasekaran B, Kolluru V, Tyagi A, Nguyen KD, Valadon CL, Shaheen SP, Kong M, Poddar T, Ankem MK, Damodaran C.  Diagnostic molecular markers predicting aggressive potential in low-grade prostate cancer.  Translational Research 2021 May;231:92-101.  doi: 10.1016/j.trsl.2020.11.014.  Epub 2020 Dec 3.  PMID: 33279680.
  3. Dekhil O, Shalaby A, Soliman A, Mahmoud A, Kong M, Barnes G, Elmaghraby A, El-Baz A.  Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns.  Medical Image Analysis 2021 Feb;68:101899.  doi: 10.1016/j.media.2020.101899.  Epub 2020 Nov 12.  PMID: 33260109.
  4. Li B, Hao J, Yan X, Kong M, Sauter ER.  A-FABP and oestrogens are independently involved in the development of breast cancer.  Adipocyte 2019 Dec;8(1):379-385.  doi: 10.1080/21623945.2019.1690827.  PMID: 31755351.  PMCID: PMC6948962.
  5. Young JL, Yan X, Xu J, Yin X, Zhang X, Arteel GE, Barnes GN, States JC, Watson WH, Kong M, Cai L, Freedman JH.  Cadmium and high-fat diet disrupt renal, cardiac and hepatic essential metals.  Scientific Reports 2019 Oct 11;9(1):14675.  doi: 10.1038/s41598-019-50771-3.  Erratum in: Scientific Reports 2020 Feb 10;10(1):2609.  MID: 31604971.  PMCID: PMC6789035.
  6. Yan X, Abdia Y, Datta S, Kulasekera KB, Ugiliweneza B, Boakye M, Kong M.  Estimation of average treatment effects among multiple treatment groups by using an ensemble approach.  Statistics in Medicine 2019 Jul 10;38(15):2828-2846.  doi: 10.1002/sim.8146.  Epub 2019 Apr 2.  PMID: 30941812.
  7. Kolluru V, Chandrasekaran B, Tyagi A, Dervishi A, Ankem M, Yan X, Maiying K, Alatassi H, Shaheen SP, C Messer J, Edwards A, Haddad A, Damodaran C.  miR-301a expression: Diagnostic and prognostic marker for prostate cancer.  UrologicOncology 2018 Nov;36(11):503.e9-503.e15.  doi: 10.1016/j.urolonc.2018.07.014.  Epub 2018 Sep 5.  PMID: 30195463.
  8. Hao J, Zhang Y, Yan X, Yan F, Sun Y, Zeng J, Waigel S, Yin Y, Fraig MM, Egilmez NK, Suttles J, Kong M, Liu S, Cleary MP, Sauter E, Li B.  Circulating adipose fatty acid binding protein is a new link underlying obesity-associated breast/mammary tumor development.  Cell Metabolism 2018 Nov 6;28(5):689-705.e5.  doi: 10.1016/j.cmet.2018.07.006.  Epub 2018 Aug 9.  PMID: 30100196.  PMCID: PMC6221972.
  9. Wu Y, Gaskins J, Kong M, Datta S.  Profiling the effects of short time-course cold ischemia on tumor protein phosphorylation using a Bayesian approach.  Biometrics 2018 Mar;74(1):331-341.  doi: 10.1111/biom.12742.  Epub 2017 Jul 25.  PMID: 28742267.  PMCID: PMC5992063.
  10. Abdia Y, Kulasekera KB, Datta S, Boakye M, Kong M.  Propensity scores based methods for estimating average treatment effect and average treatment effect among treated: A comparative study.  BiometricsJournal 2017 Sep;59(5):967-985.  doi: 10.1002/bimj.201600094.  Epub 2017 Apr 24.  PMID: 28436047.
  11. Wan Y, Datta S, Lee JJ, Kong M.  Monotonic single-index models to assess drug interactions.  Statistics in Medicine 2017 Feb 20;36(4):655-670.  doi: 10.1002/sim.7158.  Epub 2016 Nov 2.  PMID: 27804146.  PMCID: PMC5217167.
  12. Chen J, Wang S, Luo M, Zhang Z, Dai X, Kong M, Cai L, Wang Y, Shi B, Tan Y.  From the Cover: Zinc deficiency worsens and supplementation prevents high-fat diet induced vascular inflammation, oxidative stress, and pathological remodeling.  ToxicologicalSciences 2016 Sep;153(1):124-36.  doi: 10.1093/toxsci/kfw110.  Epub 2016 Jun 30.  PMID: 27370414.
  13. Kong M, Rai SN, Bolli R.  Statistical methods for selecting maximum effective dose and evaluating treatment effect when dose-response is monotonic.  Statistics in Biopharmaceutical Research 2014 Jan 2;6(1):16-29.  doi: 10.1080/19466315.2013.826596.  PMID: 25067994.  PMCID: PMC4110746.
  14. Becker LE, Lu Z, Chen W, Xiong W, Kong M, Li Y.  A systematic screen reveals microRNA clusters that significantly regulate four major signaling pathways.  PLoSOne 2012;7(11):e48474.  doi: 10.1371/journal.pone.0048474.  Epub 2012 Nov 8.  PMID: 23144891.  PMCID: PMC3493556.
  15. Kong M, Yan J.  Modeling and testing treated tumor growth using cubic smoothing splines.  Biometrical Journal 2011 Jul;53(4):595-613.  doi: 10.1002/bimj.201000098.  Epub 2011 May 23.  PMID: 21604288.
  16. Kong M, Lee JJ.  Applying Emax model and bivariate thin plate splines to assess drug interactions.  Frontiers in Bioscience (Elite Ed) 2010 Jan 1;2:279-92.  doi: 10.2741/e90.  PMID: 20036878.  PMCID: PMC4203317.
  17. Salvador C, Li B, Hansen R, Cramer DE, Kong M, Yan J.  Yeast-derived beta-glucan augments the therapeutic efficacy mediated by anti-vascular endothelial growth factor monoclonal antibody in human carcinoma xenograft models.  ClinicalCancerResearch 2008 Feb 15;14(4):1239-47.  doi: 10.1158/1078-0432.CCR-07-1669.  PMID: aza18281559.  PMCID: PMC2394864.