## Coordinating Organizer

Breanna Shi

Shi00231@umn.edu

- 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

### Target Audience

Undergraduates,graduate students and postdocs with an interest in learning about Bayesian statistics and applying Bayesian Networks to microbiome data.

### Organizer(s)

Breanna Shi

Christoper Dean

Jesse Elder

### Session Plan (Four Hours)

Introduction(30 mins)

- Introduce the mathematical basis of Bayesian Statistics
- Importance and utility of networks

General Applications(1 hour)

- 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)

Interactivity(2.5 hour)

- 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

### Learning Objectives

- What is Bayesian Statistics and how can it be used in real-world scenarios?
- How BNs are applied to â€˜omics data?
- Step-by-step how to apply BNs to AMR count data
- Utilize Bayesian networks to understand microbiome and AMR relationships