Gabriel J. Odom
High-Dimensional Statistics, Statistical Genetics, R/Bioconductor Package Development, Data Science, Matrix Theory, Bayesian Statistics, Spatial/Time Series.
Rev. Dr. Gabriel J. Odom is an Assistant Professor of Biostatistics in the Department of Biostatistics at Florida International University’s Stempel College of Public Health. He is a statistician and data scientist with primary research area in software, algorithms, and methods for high-dimensional and high-throughput (-omics) data. His current research applications are in the areas of multi-omics integration, pathway clustering, and genome-wide / epigenome-wide analyses, and he publishes open-source software packages related to these applications through the Bioconductor project. Dr. Odom completed his doctoral work in statistical science at Baylor University in 2017 under Prof. Dean M. Young and Prof. Amanda S. Hering. He completed his postdoctoral training in biostatistics in the Department of Public Health Sciences, Division of Biostatistics at the University of Miami’s Miller School of Medicine under Prof. Steven Chen and Prof. Lily Wang in 2019. He is also an ordained and active Eastern-rite (Maronite) Catholic presbyter.
- University of Miami, Postdoctoral Research (Biostatistics)
- Baylor University, PhD (Statistical Science)
- The International Miracle Institute, Th.D. (Sacred Theology)
- The University of West Florida, BS (Financial Mathematics and Statistics)
- The University of West Florida, BA (Economics; Honors Interdisciplinary Arts)
“coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes.” Gomez, L., Odom, G.J., Liu, L., Gao, Z., Chen, X., and Wang, L. Nucleic Acids Research. 2019. https://doi.org/10.1093/nar/gkz590.
“pathwayPCA: integrative pathway analysis with modern PCA methodology and gene selection.” Odom, G.J., Ban, J., Liu, L., Wang, L., and Chen, X. Bioconductor, version 0.99.5. 2019. http://dx.doi.org/10.18129/B9.bioc.pathwayPCA.
“An evaluation of supervised methods for identifying DMRs in Illumina methylation arrays.” Saurav, M.*, Odom, G.J.*, Gao, Z., Chen, X., and Wang, L. Briefings in Bioinformatics. 2018. http://dx.doi.org/10.1093/bib/bby085. (* signifies co-first-authorship)
“Multi-state multivariate statistical process control.” Odom, G.J., Newhart, K.B., Cath, T.Y., and Hering, A.S. Applied Stochastic Models in Business and Industry. 2018. https://doi.org/10.1002/asmb.2333.