Our goal is to train statistically oriented individuals (Biostatisticians, Statisticians, Signal Processors, etc.) to function as independent researchers in a multi disciplinary environment focusing on Nutrition and cancer. To achieve this goal we have assembled a team of researchers specializing in Biostatistics/Statistics, Bioinformatics/Biomedical Imaging and the biology of Nutrition and cancer. Our basic premise is that multidisciplinary teams working on Nutrition and cancer will benefit enormously by inclusion of biologically knowledgeable biostatisticians, and especially biostatisticians who are familiar at a fairly deep level with the biological mechanisms of cancer of most interest to nutritionists. This basic premise is supplemented by our observation that there exist few biostatisticians of the desired type. We will create a cadre of statistically oriented researchers who understand the mechanisms of action in the relationship between Nutrition and cancer. Such understanding will allow our trainees to contribute at the highest level to the design and analysis of experiments in the area, and to develop fine-tuned statistical methods truly appropriate for the experimental data. Through a combination of didactic course work, seminars and research experiences, we expect our trainees to be able to make important contributions in the development of statistical methods targeted to experiments in nutrition and cancer, and to function as a true collaborator in teams of biologists, instead of merely as a specialist in setting sample sizes and performing data analysis of simple experiments. The program plan is designed to: (a) provide via courses and seminars systematic training in biology as it relates to nutrition and cancer, including cDNA microarray image analysis technology; (b) provide directed research experience with statistical mentors from the Department of Statistics, nutritionist mentors from the Faculty of Nutrition, and bioinformatics/biomedical imaging mentors from the Biomedical Imaging group in the Department of Electrical Engineering; provide intensive laboratory experience by having the trainees work closely with one of the Human Nutrition laboratories, including attending weekly laboratory meetings but more generally becoming a full member of the laboratory team; (d) promote, through participation in seminar series, interaction among the trainees and among graduate students and postdoctoral researchers in nutrition.
| Mohsenizadeh, Daniel N; Dehghannasiri, Roozbeh; Dougherty, Edward R (2018) Optimal Objective-Based Experimental Design for Uncertain Dynamical Gene Networks with Experimental Error. IEEE/ACM Trans Comput Biol Bioinform 15:218-230 |
| Triff, Karen; McLean, Mathew W; Callaway, Evelyn et al. (2018) Dietary fat and fiber interact to uniquely modify global histone post-translational epigenetic programming in a rat colon cancer progression model. Int J Cancer 143:1402-1415 |
| Shah, Manasvi S; Kim, Eunjoo; Davidson, Laurie A et al. (2016) Comparative effects of diet and carcinogen on microRNA expression in the stem cell niche of the mouse colonic crypt. Biochim Biophys Acta 1862:121-34 |
| Ekenna, Chinwe; Thomas, Shawna; Amato, Nancy M (2016) Adaptive local learning in sampling based motion planning for protein folding. BMC Syst Biol 10 Suppl 2:49 |
| Huque, Md Hamidul; Bondell, Howard D; Carroll, Raymond J et al. (2016) Spatial regression with covariate measurement error: A semiparametric approach. Biometrics 72:678-86 |
| Zoh, Roger S; Mallick, Bani; Ivanov, Ivan et al. (2016) PCAN: Probabilistic correlation analysis of two non-normal data sets. Biometrics 72:1358-1368 |
| Wang, Lei; Hou, Yongqing; Yi, Dan et al. (2015) Beneficial roles of dietary oleum cinnamomi in alleviating intestinal injury. Front Biosci (Landmark Ed) 20:814-28 |
| Mohsenizadeh, Daniel N; Hua, Jianping; Bittner, Michael et al. (2015) Dynamical modeling of uncertain interaction-based genomic networks. BMC Bioinformatics 16 Suppl 13:S3 |
| Andersen, Synne M; Assaad, Houssein I; Lin, Gang et al. (2015) Metabolomic analysis of plasma and liver from surplus arginine fed Atlantic salmon. Front Biosci (Elite Ed) 7:67-78 |
| Li, Haocheng; Kozey Keadle, Sarah; Staudenmayer, John et al. (2015) Methods to assess an exercise intervention trial based on 3-level functional data. Biostatistics 16:754-71 |
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