Prabhakar Chalise, Ph.D.
Assistant Professor, Department of Biostatistics
M.Sc., Statistics, Mathematics, Tribhuvan University, Kathmandu, Nepal
M.S., Statistics, University of Arkansas, Fayetteville, AR
Ph.D., Biostatistics, Florida State University, Tallahassee, FL
PostDoc, Statistical Genetics, Mayo Clinic, Rochester, MN
Survival Analysis, Statistical Genetics and Genomics, Computational Statistics, Methods in Biostatistics
Personal Mission Statement
My primary research interest is in the development and application of statistical methods to health sciences research. I work closely in statistical genetics and genomics, survival analysis, experimental design, clinical trial, multivariate analysis and computational statistics. 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 and therefore it is essential to develop adequate modeling approaches that will be able to utilize 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 genotypic and phenotypic levels.
1. Chalise P, Batzler A, Abo R, Wang L and Fridley B. 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, July/August 2012.
2. Fridley BL, Chalise P, Tsai Y-Y, Sun Z, Vierkant RA, Larson MC, Cunningham JM, Iversen ES, Fenstermacher D, Barnholtz-Sloan J, Asmann Y, Risch HA, Schildkraut JM, Phelan CM, Sutphen R, Sellers TA and Goode EL. Germline copy number variation and ovarian cancer survival. Frontiers in Cancer Genetics, 3:142. doi:10.3389/fgene.2012.00142, 2012.
3. Chalise P and Fridley B. Comparison of performances of various penalty functions on Sparse Canonical Correlation Analysis. Computational Statistics and Data Analysis, 56: 245-254, 2012.
4. Chalise P, Chicken E and McGee D. Baseline Age Effect on Parameter Estimates in Cox Model. Journal of Statistical Computation and Simulation, 82(12):1767-1774, 2012.
5. Chalise P, Chicken E and McGee D. Performance and Prediction for Varying Survival Time Scales. Communications in Statistics -Simulation and Computation, 42(3): 636-649, 2013
6. Breheny P, Chalise P, Batzler A, Wang L and Fridley B. Genetic association studies of copy number variation: should assignment of copy number states precede testing? PLoS ONE 7(4): e34262. doi:10.1371/journal.pone.0034262, 2012.
7. Holzinger E, Dudek S, Fridley B, Chalise P, Torstenson E and Ritchie M. Comparison of Methods for Meta-dimensional Data Analysis Using In-Silico and Biological Data Sets. EvoBIO LNCS 7246, 134-143, doi:10.1007/978-642-29066-4_12, 2012
8. Cappendijk SLT, Carrier N., Miller GF, Chalise P, Pirvan DF, Santos AA, Hallquist M, James JR. In vivo nicotine exposure in the zebra finch; a promising innovative animal model to use in neurodegenerative disorders related research. Pharmacology, Biochemistry and Behavior, 96: 152-159, 2010.
9. Chicken, E., Chalise, P. and Loper, D. Conduit prevalence in the Woodville Karst Plain. ASCE 327: 303-312, doi: 10.1061/41003(327)29, 2008.
Department of Statistics, Florida State University, http://www.stat.fsu.edu/
Tribhuvan University, http://www.tribhuvan-university.edu.np/
American Statistical Association, http://www.amstat.org/
Eastern North American Region/International Biometric Society, http://www.enar.org/
International Genetic Epidemiologic Society, http://www.geneticepi.org/