This 5 year program is designed to continue to train predoctoral (PhD) students in statistical genomics with the major emphasis in cancer genomics. The goal is to train biostatisticians in the biology, etiology, and genetics of cancer, as well as to train them to conduct state-of-the-art biostatistical methodologic research relevant to the genomics of cancer as well as in related areas of genomics. The goal is to also produce biostatisticians who can collaborate with other scientific researchers and oncologists on research issues related to genomics and cancer. The typical predoctoral trainee will be a college graduate or master's level graduate with an excellent academic record appropriate for this training area. Funding is requested for the support of 5 predoctoral trainees. The Department of Biostatistics at UNC is one of the largest in the world, and has highly qualified personnel and the available facilities to provide the most comprehensive predoctoral and postdoctoral training in this research area. Several members from the Department of Genetics and the Bioinformatics and Computational Biology (BCB) program will be deeply involved in all phases of this training program and will play an integral role in this training program.
This training grant clearly has major relevance in public health as it is designed to train the next wave of biostatistical researchers in cancer genomics, which is one of the largest and growing areas of biomedical research, in which the demand for well trained biostatistical researchers is much greater than the supply.
|Ankerst, Donna P; Goros, Martin; Tomlins, Scott A et al. (2018) Incorporation of Urinary Prostate Cancer Antigen 3 and TMPRSS2:ERG into Prostate Cancer Prevention Trial Risk Calculator. Eur Urol Focus :|
|O'Brien, Jonathon J; Gunawardena, Harsha P; Qaqish, Bahjat F (2018) Row versus column correlations: avoiding the ecological fallacy in RNA/protein expression studies. Brief Bioinform 19:946-953|
|O'Brien, Jonathon J; Gunawardena, Harsha P; Paulo, Joao A et al. (2018) The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments. Ann Appl Stat 12:2075-2095|
|Bower, Jacquelyn J; Vance, Leah D; Psioda, Matthew et al. (2017) Patterns of cell cycle checkpoint deregulation associated with intrinsic molecular subtypes of human breast cancer cells. NPJ Breast Cancer 3:9|
|Qaqish, Bahjat F; O'Brien, Jonathon J; Hibbard, Jonathan C et al. (2017) Accelerating high-dimensional clustering with lossless data reduction. Bioinformatics 33:2867-2872|
|Lee, Clara Nan-Hi; Deal, Allison M; Huh, Ruth et al. (2017) Quality of Patient Decisions About Breast Reconstruction After Mastectomy. JAMA Surg 152:741-748|
|Bryant, Christopher; Zhu, Hongtu; Ahn, Mihye et al. (2017) LCN: a random graph mixture model for community detection in functional brain networks. Stat Interface 10:369-378|
|Carson, Johnny L; Zhou, Laura; Brighton, Luisa et al. (2017) Temporal structure/function variation in cultured differentiated human nasal epithelium associated with acute single exposure to tobacco smoke or E-cigarette vapor. Inhal Toxicol 29:137-144|
|Smith, Ikuko T; Townsend, Leah B; Huh, Ruth et al. (2017) Stream-dependent development of higher visual cortical areas. Nat Neurosci 20:200-208|
|Rao, Shangbang; Ibrahim, Joseph G; Cheng, Jian et al. (2016) SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging. J Comput Graph Stat 25:1195-1211|
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