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Prabhakar Chalise, PhD

Prabhakar Chalise portrait
Associate Professor, Biostatistics & Data Science

Professional Background

Dr. Prabhakar Chalise is an Associate Professor and Assistant Director of Graduate Education in the Department of Biostatistics and Data Science. Dr. Chalise has collaborated in numerous internally and externally funded projects. The projects range from wide varieties of biomedical studies including both clinical and molecular researches.

Education and Training
  • PhD, Statistics, Florida State University, Tallahassee , FL
  • Post Doctoral Fellowship, Statistical genetics and genomics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
Professional Affiliations
  • University of Kansas Alzheimer's Disease Center, Member, 2018 - Present
  • University of Kansas Cancer Center , Member, 2014 - Present
  • International Genetic Epidemiology Society, Member, 2010 - Present
  • International Biometric Society, Member, 2009 - Present
  • American Statistical Association, Member, 2008 - Present



My primary research interest is in utilizing my training in biostatistics and quantitative sciences to understand the biology of living systems. I work closely in the studies of molecular basis of the disease using various types of genetics and genomics data. Most of such datasets are high dimensional in nature which include SNP, mRNA expression, microRNA, DNA methylation, and protein expression. Besides molecular data analysis, I have also worked in many clinical trial studies starting from the design phase of the study to analyses and interpretation. Biomedical research is growing increasingly advanced in unprecedented ways due to the advent of high throughput technologies. Such high throughput experiments generate large volume of data. It is essential to develop adequate modeling approaches that will be able to leverage all available information in order to explain the correct disease risk association. My research is focused in developing statistical and computational methods in analyzing such data to address frontier problems in biomedical research both in molecular and clinical levels.

  • Chalise, P, Fridley, B., L. 2017. Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm. PloS one, 12 (5), e0176278. | pone.0176278
  • Chalise, P, Ni, Y, Fridley, B. 2020. Network-based integrative clustering of multi-level ‘Omic data using non-negative matrix factorization. Computers in Biology and Medicine, 118 (103625).
  • Cancer Genome Atlas Research Network, (TCGA), Co-author Chalise, P. 2017. Integrated genomic and molecular characterization of cervical cancer. Nature, 543 (7645), 378-384.
  • Chinnappan, M, Gunewardena, S, Chalise, P, Dhillon, N., K. 2019. Analysis of lncRNA-miRNA-mRNA Interactions in Hyper-proliferative Human Pulmonary Arterial Smooth Muscle Cells. Scientific Reports.
  • Swerdlow, R., H, Hui, D, Chalise, P, Sharma, P, Wang, X, Andrews, S., J, Pa, J, Mahnken, J, Morris, J, Wilkins, H., M, Burns, J., M, Goate, A, Michaelis, M., L, Michaelis, E., K. 2020. Exploratory Analysis of mtDNA Haplogroups in Two Alzheimer’s Disease Longitudinal Cohorts. Alzheimer's and Dementia, 16, 1164-1172.
  • Hamilton-Reeves, J., M, Bechtel, M., D, Hand, L., K, Schleper, A, Yankee, T., M, Chalise, P, Lee, E., K, Mirza, M, Wyre, H, Griffin, J, Holzbeierlein, J. 2016. Effects of immunonutrition for cystectomy on immune response and infection rates: a pilot randomized controlled clinical trial. European urology, 69 (3), 389-392.
  • Chalise, P, Koestler, D., C, Bimali, M, Yu, Q, Fridley, B., L. 2014. Integrative clustering methods for high-dimensional molecular data. Translational cancer research, 3 (3), 202-216.
  • Chalise, P, Batzler, A, Abo, R, Wang, L, Fridley, B., L. 2012. Simultaneous analysis of multiple data types in pharmacogenomic studies using weighted sparse canonical correlation analysis. Omics: a journal of integrative biology, 16 (7-8), 363-373.
  • Chalise, P, Raghavan, R, Fridley, B., L. 2016. InterSIM: Simulation tool for multiple integrative ‘omic datasets’. Computer methods and programs in biomedicine, 128, 69-74.
  • Chalise, P, Fridley, B., L. 2012. Comparison of penalty functions for sparse canonical correlation analysis. Computational statistics & data analysis, 56 (2), 245-254.