Jennifer Clarke
- Director of the Quantitative Life Sciences Initiative
- Professor, Department of Statistics
jclarke3@unl.edu
402-472-2512
Dr. Clarke's research interests encompass statistical methodology (with an emphasis on high dimensional and predictive methods), statistical computation, bioinformatics/computational biology, multi-type data analysis, data mining/machine learning, and bacterial genomics/metagenomics (gut function initiative). She is the director of the Quantitative Life Sciences Initiative.
Featured Publications
LaTourrette K, Stengel A, and Clarke J. Student-led workshops: Filling skills gaps in computational research for life scientists. Nat Sci Educ. 2021; 50:e20052. Available from: https://doi.org/10.1002/nse2.20052
Dutta E, Loy D, Deal CA, Wynn EL, Clawson ML, Clarke J, Wang B. Development of a Multiplex Real-Time PCR Assay for Predicting Macrolide and Tetracycline Resistance Associated with Bacterial Pathogens of Bovine Respiratory Disease. Pathogens 2021, 10, 64. Available from: https://doi.org/10.3390/pathogens10010064
Dobra A*, Valdes C,*, Ajdic D, Clarke B, and Clarke J. Assessing Statistical Dependence within Microbial Communities with Clique Log-Linear Models. Annals of Applied Statistics, 2019, 13(2): 931-957. Available from: http://doi.org/10.1214/18-AOAS1229
Penas C, Maloof ME, Stathias V, Long J, Tan SK, Mier S, Fan Y, Valdes C, Rodriguez-Blanco J, Chiang CM, Robbins D, Liebl D, Lee J, Hatten M, Clarke J, and Ayad N. Time-series Modeling of Cell Cycle Exit Identifies Brd4-dependent Regulation of Cerebellar Neurogenesis. Nature Communications 2019, 10, 3028. Available from: http://doi.org/10.1038/s41467-019-10799-5
Clarke B and Clarke J. Predictive Statistics: Analysis and Inference beyond Models (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge: Cambridge University Press, 2018. Available from: http://doi.org/10.1017/9781139236003