Department of Statistics
Our Faculty and Staff
Yen-Yi Ho
| Title: | Associate Professor |
| Department: | Statistics, Biological Sciences College of Arts and Sciences |
| Email: | hoyen@stat.sc.edu |
| Phone: | 803-777-5163 |
| Office: | LeConte 216A |
| Resources: | My Website Curriculum Vitae [pdf] Department of Statistics |
Research Interests
I enjoy working with data. I am currently working as an assistant professor at the Department of Statistics with a joint appointment in Biological Sciences at the University of South Carolina. I received a Ph.D. from the Department of Biostatistics at the Johns Hopkins University. My general research interests include computational biology, statistical genetics and statistical consulting. I am interested in developing statistical methods and algorithms for analyzing genetic data generated by high-throughput technologies, and gene pathway/network enrichment analysis. I also participate in collaborative research and provide statistical support for a wide range of exciting research topics such as genetic mechanisms for Hirschsprung disease, frailty in older women, and chemopreventive strategies for cancer.
Selected Publications
1. Ho Y.-Y., Parmigiani, G., Louis, T.A., Cope, L.M. (2010). Modeling Liquid Association. Biometrics 67, 133-141. doi: 10.1111/j.1541-0420.2010.01440.x.
2. Ho Y.-Y., Matteini A.M., Beamer B., and Fried L., et al. (2011). Exploring biologically relevant pathways in frailty. Journal of Gerontology A Biological Sciences and Medical Sciences 66, 975-979.
3. Ho Y.-Y., Cope, L.M., Parmigiani, G. (2014). Modular network construction using eQTL data: an analysis of computational costs and benefits. Frontiers in Genetics 5, 40. doi: 10.3389/fgene.2014.00040
4. Ho Y.-Y., Baechler E.C., Ortmann W., Behrens T.W., Graham R.R., Bhangale T.R., Pan W. (2014). Using Gene Expression to Improve the Power of Genome-Wide Association Analysis. Human Heredity 78, 94-103. doi: 10.1159/000362837
5. Gunderson T.*, Ho Y.-Y.* (2014) An efficient algorithm to explore liquid association on a genome-wide scale. BMC Bioinformatics 15, 371.
6. Ho Y.-Y., O'Connell M., Guan W., Basu S. (2015) Powerful Association Test Combining Rare Variant and Gene Expression Using Family Data from Genetic Analysis Workshop 19. Genetic Analysis Workshop 19 Proceedings 9 Suppl 8, S33.
7. Ho Y.-Y., LaRue R.S., Timothy T. Starr, Largaespada D.A. (2016) Case-oriented pathways analysis in pancreatic adenocarcinoma using data from a sleeping beauty transposon mutagenesis screen. BMC Medical Genomics 9, 16.
8. Ho Y.-Y.*, Vo T.N.*, Chu H., LeSage M.G., Luo X., Le C.T. (2016) A Bayesian hierarchical model for demand curve analysis. *These authors contributed equally to this paper. Statistical Methods in Medical Research. DOI: 10.1177/0962280216673675
9. Abbott K, Ho Y.-Y., Erickson J. (2017) Automatic Health Record Review to Identify Gravely Ill Social Security Disability Applicants. Journal of the American Medical Informatics Association 24, 709-716.
10. Nallandhighal S., Park G.S., Ho Y.-Y., Opoka R. O., John C. C., Tran T.M. (2018) Blood transcriptional signatures related to erythropoietic and Nrf2 pathways differ between cerebral malaria and severe malarial anemia.
Book Chapter
1. Ho Y.-Y., Cope L., Dettling M., and Parmigiani G. (2007). Statistical methods for identifying differentially expressed gene combinations. Methods in Molecular Biology 408, 171-191.
Software
1. Ho, Y.-Y. (2009). LiquidAssociation: R/Bioconductor package for estimating liquid association using the conditional normal model. Available at http://www.bioconductor.org.
2. Gunderson T.* (2014). fastLiquidAssociation: R/Bioconductor package for exploring liquid association on a genome-wide scale. Available at http://www.bioconductor.org.