- Introduction to Bayesian statistics and probabilistic models
- Comparative discussion of BNs packages in R
- Generating and testing hypotheses using BNs
- Applications of BNs to metabolomic and microbiome data
Undergraduates,graduate students and postdocs with an interest in learning about Bayesian statistics and applying Bayesian Networks to microbiome data.
- Introduce the mathematical basis of Bayesian Statistics
- Importance and utility of networks
- Bayesian Networks - How do these ideas combine into a relevant application?
- Comparison of techniques for applying Bayesian Networks (R, Python, Matlab)
- Discussion: The challenge of choosing priors
- Second Introduction: Microbiome & Antimicrobial Resistance (AMR)
- Brief overview of empirical Bayesian Statistics
- Different approaches to BNs (for metagenomic data, specifically)
- Walkthrough of Meta AMR pipeline
- Use Microbiome count data make a network using Hill-Climbing algorithm in R
- Use output of Hill-Climbing algorithm to make a Gephi file
- Create a network in Gephi software
- Use Bayesian theorem to find the strength of significant connections
- What is Bayesian Statistics and how can it be used in real-world scenarios?
- How BNs are applied to ‘omics data?
- Step-by-step how to apply BNs to AMR count data
- Utilize Bayesian networks to understand microbiome and AMR relationships