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This project is a research component of the GenoPhenoEnvo NSF HDR award. It’s written in the R language and uses the Bayesian Network library
bnlearn to construct diacyclic graphs for the phenotypic, genomic, and environmental data compiled by the TERRA-REF Project. The data and code are freely available on GitHub and, when the project is completed, the Docker container and data will be freely available to use on the CyVerse Discovery Environment.
As a trained microbial ecologist and microbiologist, I am very interested in how microbial communities function. My doctoral work focused on recirculating aquaculture system microbiomes, as well as aquaponic microbiomes. Prior work in this area can be found here. This is an on-going interest that I am actively seeking funding to continue.
Developed for the University of Arizona Data Science Fellows Program this course uses the CyVerse Discovery Environment and docker to provide a brief introduction to statistical analyses in the R language for postdoctoral researchers in computer science and biology. Topics covered include basic regression, distribution modeling, markov chains, and Bayesian Belief Networks.
For any recent publication activity please visit my Google Scholar page.