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Yanming Li, Ph.D.

Assistant Professor, Department of Biostatistics & Data Science
B.S., Mathematics, University of Science and Technology of China, China
M.A., Mathematics, Dartmouth College, Hanover, NH
M.S. Statistics and Probability, Michigan State University, East Lansing, MI
Ph.D., Biotatistics, University of Michigan, Ann Arbor, MI

2010 - Rackham travel award, University of Michigan
2010 & 2011 - ENAR diversity workshop travel award, ENAR
2013 - Rackham travel award, University of Michigan
2018 - Awarded nominee of the University of Michigan Rogel Cancer Center for NCCN Foundation 2019 Young Investigator Award (YIA) competition
2019 - NCCN Young Investigator Award, National Comprehensive Cancer Network
2019 - Awarded nominee of the University of Michigan Rogel Cancer Center for the 2020 Phi Beta Psi Research Grant competition


Research Focus

Research Focus High-dimensional Data Analysis; Variable Selection; Survival Analysis with High-Dimensional Predictors; Weak Signal Detection, Estimation and Their Effects in Prediction; Probabilistic Graphical Models; Computational Statistics; Cancer Genomics; Neuroimaging-Genomics.

Personal Mission Statement
My primary research interest has been focused on developing statistical methods and computational algorithms for analyzing big and complex data, including cancer-genomic, neuroimaging-genomics, survival and other clinical data. I am also experienced in statistical consulting, conducting Genome-Wide Association Studies (GWAS) and Brain-Wide Association Studies (BWAS), analyzing Next-Generation Sequencing (NGS) data and claim and electronic health record data such as measures of readmission, hospitalization and mortality rates arising from CMS End-Stage Renal Diseases (ESRD) data.

Top 15 Publications

1. Xia L, He K, Li Y, Kalbeisch J D. Accounting for total variation and robustness in profiling health care providers. Biostatistics 2020. Accepted.

2. Morris E, He K, Li Y, Li Y, Kang J. SurvBoost: An R package for high-dimensional variable selection in the stratified proportional hazards model via gradient boosting. The R Journal 2020. In Press.

3. Li Y. A Local-Network Guided Linear Discriminant Analysis for Classifying Lung Cancer Subtypes using Individual Genome-Wide Methylation Profiles. In: Arai K., Bhatia R., Kapoor S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. pp 676-687. FTC 2019. Advances in Intelligent Systems and Computing, vol 1069. Springer, Cham.

4. He K, Dahlerus C, Xia L, Li Y, Kalbeisch J D. The Profile inter-unit reliability. Biometrics. 2019; 76(2) 654-663.

5. Li Y, Hong HG, Li Y. Multiclass linear discriminant analysis with ultrahigh-dimensional features. Biometrics. 2019;75(4):1086-1097.PubMed PMID: 31009070; PubMed Central PMCID: PMC6810714.

6. Li Y, Hong HG, Ahmed SE, Li Y. Weak signals in high-dimension regression: detection, estimation and prediction. Appl Stoch Models Bus Ind. 2019;35(2):283-298. PubMed PMID: 31666801; PubMed Central PMCID: PMC6821396.

7. Li Y, Gillespie BW., Shedden K, Gillespie J. Calculating profile likelihood estimates of the correlation coefficient in the presence of left, right or interval censoring and missing data. The R Journal. 2018; 10(2):159-179.

8. Kalbeisch J D, He K, Xia L, Li Y. Does the Inter-Unit Reliability (IUR) Measure Reliability. Health Services and Outcomes Research Methodology. 2018; 18(3) 215-225. DOI:10.1007/s10742-018-0185-4.

9. Li Y, Hong H G, Li Y. (2017) Discussion of the paper Post Selection Shrinkage Estimation for High Dimensional Data Analysis. Applied Stochastic Models in Business and Industry, 33,126-129.

10. Li Y, Nan B, Zhu J. Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure. Biometrics. 2015;71(2):354-363. PubMed PMID: 25732839; PubMed Central PMCID: PMC4479976.
11. He K, Li Y, Zhu J, Liu H, Lee JE., Amos CI., Hyslop T, Jin J, Wei Q, Li Y. Variable selection and false discovery control: with applications in genome-wide association studies. Bioinformatics. 2015; 32, 50-57.

12. Das S, Stuart P, Ding J, Tejasvi T, Li Y, Tsoim L C, Chandran V, Fischer J, Helms C, Duffin K, Voorhees J, Bowcock A, Krueger G, Lathrop G M, Nair R P, Rahman P, Abecasis G R, Gladman D, Elder J T. Fine Mapping of Eight Psoriasis Susceptibility Loci. European Journal of Human Genetics,2015; 23 (6), 844-853.

13. Stuart P E, Nair R P, Tsoi L C, Tejasvi T, Das S, Kang H M, Ellinghaus E, Chandran V, Callis-Duffin K, Ike R, Li Y, Wen X, Enerback C, Gudjonsson J E, Koks S, Kingo K, Esko T, Mrowietz U, Reis A, Wichmann H E, Gieger C, Hoffmann P, Nothen M M, Winkelmann J, Kunz M, Moreta E G, Mease P J, Ritchlin C T, Bowcock A M, Krueger G G, Lim H W, Weidinger S, Weichenthal M, Voorhees J J, Rahman P, Gregersen P K, Franke A, Gladman D D, Abecasis G R, Elder J T. Genome-wide association analysis of psoriatic arthritis and cutaneous psoriasis reveals differences in their genetic architecture. The American Journal of Human Genetics. 2015. 97 (6), 816-836.

14. Tsoi L S, Spain S, Knight J, Ellinghaus E, Stuart P, Capon F, Ding J, Li Y, Tejasvi T, Gudjonsson J, Kang H M, Allen M, McManus R, Novelli G, Samuelsson L, Schalkwijk J, Stahle M, Burden A, Smith C, Cork M, Estivill X, Bowcock A, Krueger G, Weger W, Worthington J, Tazi-Ahnini R, Nestle F, Hayday A, Hoffmann P, Winkelmann J, Wijmenga C, Langford C, Edkins S, Andrews R, Blackburn H, Strange A, Band G, Pearson R, Vukcevic D, Spencer C, Deloukas P, Mrowietz U, Schreiber S, Weidinger S, Koks S, Kingo K, Esko T, Metspalu A, Lim H, Voorhees J, Weichenthal M, Chandran V, Rosen C, Rahman P, Gladman D, Griffiths C, Reis A, Kere J, Nair R, Franke A, Barker J, Abecasis G R, Elder J T, Trembath, R. Identiffcation of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nature Genetics, 2012; 44(12).1341-1348.

15. Chen W, Stambolian D, Edwards A, Branham K, Othman M, Jakobsdottir J, Tosakulwong N, Pericak-Vance M, Campochiaro P, Klein M, Tan P, Conley Y, Kanda A, Kopplin L, Li Y, Augustaitis K, Karoukis A, Scott W, Agarwal A, Kovach J, Schwartz S, Postel E, Brooks M, Baratz K, Brown W, Brucker A, Orlin A, Brown G, et al. Genetic variants near TIMP3 and high-density lipoprotein associated loci influence susceptibility to age-related macular degeneration. Proceedings of the National Academy of Sciences. 2010;107(16), 7401-7406.

Google Scholar Profile Page https://scholar.google.com/citations?hl=en&user=01SVfhwAAAAJ&view_op=list_works&gmla=AJsN-F5Ro7J9BWA3udN1wJ20IuLYTit4xArwybXP6exIJLDO9dolAKCbrsnzpyVqxRagbnEfGuNFW6LT-l2bvPnBid7ESwVVDAaA1FXl0nlf8fbjkQ6AnrY

Last modified: Jun 24, 2020

Contact

Yanming Li, Ph.D.
Assistant Professor, Department of Biostatistics & Data Science

P: 913-588-4703
yli8@kumc.edu

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