Rachael Hageman Blair
PhD
Associate Director for Education
Associate Professor
Mathematical biology; optimization; numerical analysis; inverse problems; statistics and scientific computing; methodology development for mathematical modeling and simulation of metabolic and genetic networks; data analysis including microarray and quantitative trait loci.
Selected Publications
- Shi T, Yu H, Hageman Blair R* (2023). Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization. Statistical Applications in Genetics and Molecular Biology 22(1).
- Liu T, Yu H, Hageman Blair R* (2022) Out-of-bag stability estimation for k-means clustering. Statistical Data Analysis and Mining 15(6): 781-796.
- Tian M., Hageman Blair R., Mu L., Bonner M., Browne R. and Yu H. A framework for stability-based module detection in correlation graphs. doi: 10.1002/sam.11495. Statistical Analysis and Data Mining, 2021; 1-15.
- Yu H. and Hageman Blair R. Scalable module detection for attributed networks with applications to breast cancer. Journal of Applied Statistics. 2020; 1-18.
- Yu H., Moharil J., Hageman Blair R. BayesNetBP and R package for probabilistic reasoning in Bayesian Networks. Journal of Statistical Software. 2020; (94)3.
- Yu H. and Hageman Blair R. Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's disease. 2019;20 (386) BMC Bioinformatics.
- Yu H., Chapman B., Di Florio A., Eischen E., Gotz D., Jacob M., Hageman Blair R. Bootstrapping estimates of stability for clusters, observations and model selection. Computational Statistics 2018; 1-24.
- Farasat A., Nikolaev A., Srihari S., Hageman Blair R. Probabilistic Graphical Models in Modern Social Networks. Social Network Analysis and Mining 2015; 5(1) 1-18.
- Hageman Blair R., Kliebenstein D.J., Churchill G.A. What can causal networks tell us about metabolic pathways? e10024258. doi: 10.1371/journal.pcbi.1002458. PLoS Computational Biology 2012; 8(4).