Our goal in this continuation proposal is to train statistically and computationally oriented individuals (Biostatisticians, Statisticians, Electrical Engineers, Computer Scientists, etc.) to function as independent researchers in a multidisciplinary environment focusing on cancer research, researchers who are trained in the fundamentals of Nutrition and Cancer. To achieve this goal we have assembled a team specializing in Biostatistics/Statistics, Bioinformatics, Computer Science, Genomic Signal Processing and the biology of Nutrition and Cancer. 1) The training is fully multidisciplinary. a) We focus on training statistically and computationally oriented individuals in the biology of Nutrition and Cancer, creating researchers who understand the underlying 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. b) The training will include initial rotations through 3 laboratories, as well as a rotation with the Texas A&M Genomics Facility, the co-director of which is a diet chemoprevention cancer biologist in our training program, followed by intensive long-term training in a single laboratory. c) We now include formal training specific to our program that emphasizes Fundamental Principles of Nutrition and Emerging Technologies and their Applications. 2) Each trainee will have at least two mentors. a) A Nutritionist whose research focuses on cancer etiology and prevention. The trainee will be expected to become a full member of the nutritionist's laboratory, including spending significant time in the laboratory, attending laboratory meetings, consulting for the graduate students and postdoctoral researchers in the laboratory, supervising the analysis of experimental data and eventually posing new problems and approaches. b) A Biostatistician, an Electrical Engineer or a Computer Scientist. Our team includes experts in the analysis of experimental data arising from Nutrition and Cancer, particularly those of a longitudinal nature;the analysis of high throughput expression data, DNA sequencing and phenotype data;the analysis of proteomics data;the construction and the control of gene regulatory networks, etc.
Our goal is to train statistically and computationally oriented individuals (Biostatisticians, Statisticians, Electrical Engineers, Computer Scientists, etc.) to function as independent researchers in a multidisciplinary environment focusing on cancer research, researchers who are trained in the fundamentals of Nutrition and Cancer. To achieve this goal we have assembled a team specializing in Biostatistics/Statistics, Bioinformatics, Computer Science, Genomic Signal Processing and the biology of Nutrition and Cancer and provide fully multidisciplinary training.
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
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