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.
|Assaad, Houssein; Yao, Kang; Tekwe, Carmen D et al. (2014) Analysis of energy expenditure in diet-induced obese rats. Front Biosci (Landmark Ed) 19:967-85|
|Cho, Youngmi; Turner, Nancy D; Davidson, Laurie A et al. (2014) Colon cancer cell apoptosis is induced by combined exposure to the n-3 fatty acid docosahexaenoic acid and butyrate through promoter methylation. Exp Biol Med (Maywood) 239:302-10|
|Zollanvari, Amin; Dougherty, Edward R (2014) Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model. Pattern Recognit 47:2178-2192|
|Ruan, Zheng; Liu, Shiqiang; Zhou, Yan et al. (2014) Chlorogenic acid decreases intestinal permeability and increases expression of intestinal tight junction proteins in weaned rats challenged with LPS. PLoS One 9:e97815|
|Tekwe, Carmen D; Carter, Randy L; Cullings, Harry M et al. (2014) Multiple indicators, multiple causes measurement error models. Stat Med 33:4469-81|
|Garcia, Tanya P; Muller, Samuel; Carroll, Raymond J et al. (2014) Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data. Bioinformatics 30:831-7|
|Zollanvari, Amin; Genton, Marc G (2013) On Kolmogorov Asymptotics of Estimators of the Misclassification Error Rate in Linear Discriminant Analysis. Sankhya Ser A 75:|
|Zollanvari, Amin; Hua, Jianping; Dougherty, Edward R (2013) Analytical Study of Performance of Linear Discriminant Analysis in Stochastic Settings. Pattern Recognit 46:3017-3029|
|Tekwe, Carmen D; Lei, Jian; Yao, Kang et al. (2013) Oral administration of interferon tau enhances oxidation of energy substrates and reduces adiposity in Zucker diabetic fatty rats. Biofactors 39:552-63|
|Garcia, Tanya P; Muller, Samuel; Carroll, Raymond J et al. (2013) Structured variable selection with q-values. Biostatistics 14:695-707|
Showing the most recent 10 out of 56 publications