Jennifer A. Tom, Ph.D.
Affiliations: | 2011 | University of California, Los Angeles, Los Angeles, CA |
Area:
Biostatistics BiologyGoogle:
"Jennifer Tom"Parents
Sign in to add mentor| Janet S. Sinsheimer | grad student | 2011 | UCLA | |
| (Bayesian Hierarchical Modeling for Massive Sequence Datasets.) | ||||
| Marc A. Suchard | grad student | 2011 | UCLA | |
| (Bayesian Hierarchical Modeling for Massive Sequence Datasets.) | ||||
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Publications
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| Williams J, Bravo HC, Tom J, et al. (2019) :Â Simulating longitudinal differential abundance for microbiome data. F1000research. 8: 1769 |
| Tom J, Chang D, Wuster A, et al. (2018) Enabling genome-wide association testing with multiple diseases and no healthy controls. Gene |
| Haug-Baltzell A, Bhangale TR, Chang D, et al. (2018) Previously reported placebo-response-associated variants do not predict patient outcomes in inflammatory disease Phase III trial placebo arms. Genes and Immunity |
| Tom JA, Reeder J, Forrest WF, et al. (2017) Identifying and mitigating batch effects in whole genome sequencing data. Bmc Bioinformatics. 18: 351 |
| Tom JA, Sinsheimer JS, Suchard MA. (2012) Does history repeat itself? Wavelets and the phylodynamics of influenza A. Molecular Biology and Evolution. 29: 1367-77 |
| Edo-Matas D, Lemey P, Tom JA, et al. (2011) Impact of CCR5delta32 host genetic background and disease progression on HIV-1 intrahost evolutionary processes: efficient hypothesis testing through hierarchical phylogenetic models. Molecular Biology and Evolution. 28: 1605-16 |
| Tom JA, Sinsheimer JS, Suchard MA. (2010) Reuse, Recycle, Reweigh: Combating Influenza through Efficient Sequential Bayesian Computation for Massive Data. The Annals of Applied Statistics. 4: 1722-1748 |