The Data Processing and Software Development Core (Core D) will compile databases, assist with data analysis, and develop new user-friendly software programs in support of the overall goals of the program project. The Core will create ?master? colorectal cancer databases that will provide a focal point for inspiring and applying new methods developed by project investigators. The Core will also work with project investigators to translate their research code into user- friendly software programs that can be implemented by outside investigators to apply new methods to their study data. The Core will develop the infrastructure related to data processing and software development necessary to carry out these goals. In general, the activities of this Core will serve a central role in integrating the methods development across projects and in disseminating program findings to the broader scientific community.

Public Health Relevance

The Data Processing and Software Development Core (Core D) will compile master databases that link individual-level colorectal cancer data with genomic annotation information and will assist project investigators with analysis of these data. The Core will also develop new user-friendly software programs that make the novel statistical methods developed by the Program available to investigators in the general scientific community.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
1P01CA196569-01A1
Application #
9072856
Study Section
Special Emphasis Panel (ZCA1)
Project Start
2016-07-01
Project End
2021-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Southern California
Department
Type
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90032
Ryser, Marc D; Min, Byung-Hoon; Siegmund, Kimberly D et al. (2018) Spatial mutation patterns as markers of early colorectal tumor cell mobility. Proc Natl Acad Sci U S A 115:5774-5779
Liu, Jie; Liang, Gangning; Siegmund, Kimberly D et al. (2018) Data integration by multi-tuning parameter elastic net regression. BMC Bioinformatics 19:369
Moss, Lilit C; Gauderman, William J; Lewinger, Juan Pablo et al. (2018) Using Bayes model averaging to leverage both gene main effects and G?×? E interactions to identify genomic regions in genome-wide association studies. Genet Epidemiol :
Ritchie, Marylyn D; Davis, Joe R; Aschard, Hugues et al. (2017) Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 186:771-777
Patel, Chirag J; Kerr, Jacqueline; Thomas, Duncan C et al. (2017) Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiol Biomarkers Prev 26:1370-1380
Thomas, Paul D (2017) The Gene Ontology and the Meaning of Biological Function. Methods Mol Biol 1446:15-24
Manrai, Arjun K; Cui, Yuxia; Bushel, Pierre R et al. (2017) Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health. Annu Rev Public Health 38:279-294
Marconett, Crystal N; Zhou, Beiyun; Sunohara, Mitsuhiro et al. (2017) Cross-Species Transcriptome Profiling Identifies New Alveolar Epithelial Type I Cell-Specific Genes. Am J Respir Cell Mol Biol 56:310-321
Ritz, Beate R; Chatterjee, Nilanjan; Garcia-Closas, Montserrat et al. (2017) Lessons Learned From Past Gene-Environment Interaction Successes. Am J Epidemiol 186:778-786
Gauderman, W James; Mukherjee, Bhramar; Aschard, Hugues et al. (2017) Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 186:762-770

Showing the most recent 10 out of 28 publications