Machine Learning on Microbiome Data: Theory and Practice

Code and data for the workshop are available HERE

Co-organizers: Tatiana Lenskaia and Sambhawa Priya

The workshop will focus on the theory and application of machine learning to microbiome datasets, including bacterial and viral communities. The following topics will be addressed:

• Introduction to supervised machine learning, and how to implement a machine learning workflow using R and Python.
• Applying machine learning algorithms (such as Random Forest) on microbiome datasets for disease risk prediction in humans.
• Introduction to bacteria-phage interactions, and how it contributes to bacterial pathogenicity.
• Applying machine learning methods to predict bacterial pathogenicity induced by phages.