Training in Computational Genomic Epidemiology of Cancer (CoGEC)

Robert C. Elston, PhD

Program Director: Robert C. Elston, PhD
Professor, Epidemiology & Biostatistics
robert.elston@case.edu

Li Li, MD, PhD

Associate Director: Li Li, MD, PhD
Professor, Family Medicine
lxl62@case.edu


Components

Fellows will spend two to three years - with two or more mentors from different disciplines - preparing for careers as independent cancer researchers. In order to meet the different needs of fellows, we have devised a core specialized curriculum comprising the following five semester-long courses, that will serve to orient each fellow to the field of Computational Genetic Epidemiology of Cancer: Introduction to Cancer Biology; Molecular Genetics of Cancer; Epidemiology: Application of Theory and Methods; Statistical Methods in Human Genetics; and Introduction to Computational Biology/Bioinformatics. Fellows may waive courses based on their backgrounds.

Because fellows will enter the program with varying education backgrounds, the program will offer additional didactic training in Computational Genomic Epidemiology of Cancer designed to enable them to broaden their knowledge in ways that are appropriate to their particular needs. In addition to the formal course offerings, numerous relevant research seminars will be available to the fellows. All fellows will be expected to submit an abstract for presentation at one or more national meetings; the expectation is that the fellow will be first author and bear primary responsibility for the associated manuscript preparation, with assistance and constructive criticism from mentors.

Fellows will obtain research experience to prepare them as independent scientists working at the forefront of Computational Genomic Epidemiology of Cancer as part of multidisciplinary teams. For each fellow, we will develop an individualized program that will give him or her training across the range of traditional disciplines that comprise Computational Genomic Epidemiology of Cancer. This research experience will be simultaneously coordinated with the didactic training described above. We anticipate that fellows with differing backgrounds and interests will take part in different research experiences. For example, fellows with a molecular genetic PhD or an MD may undertake wet-lab experiments as part of their research, while fellows with a mathematics or statistics PhD may undertake mathematical modeling as part of their research. However, in all cases fellows will also be required to develop expertise in the computational aspects of their projects.