Systems immunology: an intro to multi-omics data integration and machine learning, online

In this course we will learn about multi-omic data analyses and how these approaches are revolutionising biomedical research.  In this course we will use a user-friendly graphical user interface (SIMON) to practically explore high-dimensional data analysis methods including machine learning. 

The course is aimed at biomedical researchers with minimal or no machine learning experience, but with background knowledge in ‘omics’ data, such as transcriptomics, proteomics, cytometry and other single-cell data analysis and planning to perform high-dimensional data analysis. By the end of the course attendees would be expected to have basic understanding on multi-omic data analysis as well as practical experience using the non-technical SIMON software package

COURSE OVERVIEW

Day 1 – Machine Learning for biomedical research

Theoretical part: Introduction to Systems Immunology and Machine Learning 

• Why use machine learning?
• What is machine learning and how is it done?
• Examples from biomedical research
• Systems Immunology examples

Practical part: installing software, downloading example data and initial exploratory analyses.

• Correlation analysis
• Multidimensional scaling
• Hierarchical clustering 

Day 2 – Practical use of ML and introduction to AI 

Practical part: installed software, downloading example data and some exploratory analysis. 

• Data exploration with ML
• Variable importance selection
• Significance testing

Theoretical part: Introduction to artificial intelligence

• Why use artificial intelligence learning?
• Examples of current/routine use of AI in healthcare
• AI in COVID-19 and pandemic preparedness 

ADDITIONAL MATERIAL 

Installation instructions: SIMON repository (link: https://github.com/genular/simon-frontend). Software is available on the website: https://genular.org/.

Related literature:

Tomic et al, JI, 2019, https://doi.org/10.4049/jimmunol.1900033

Tomic et al, Patterns, 2021, https://doi.org/10.1016/j.patter.2020.100178

Step-by-step analysis instructions: SIMON manuscript (link: https://www.cell.com/patterns/fulltext/S2666-3899(20)30242-7)

Instruction videos (link: https://genular.org/simon-machine-learning-knowledge-base/instruction-videos/)

COURSE OBJECTIVES

•  complete end-to-end machine learning analysis using SIMON

• learn how to prepare data for analysis

• understand the importance of reducing the dimensionality using appropriate methods

• learn how to properly evaluate predictive models using performance metrics

• perform exploratory analysis

PARTICIPANT NUMBERS

20

ATTENDANCE SURVEY ON COMPLETION

It is now a requirement that you complete the three short questions in the survey you receive after attending the course. Once you have submitted the survey, you will be sent an email with a link to your attendance certificate. This is to ensure we receive the feedback we need to evaluate and improve our courses. Survey results are downloaded and stored anonymously.

feedback from previous sessions

The theoretical aspect was very good with valuable information. The practical demonstrations were also useful.

The instructor did an excellent job! He was very patient and gave detailed explanations, especially when we were stuck with downloading the software

 The theoretical aspect was very good with valuable information. The practical demonstrations were also useful.