Adriana Tomic, PhD
Systems Immunology, University of Oxford
Oxford, UK
Email: info@adrianatomic.com
In this course, we will learn how to use SIMON – our recently developed open-source software for the application of machine learning to biological and clinical data.
Why SIMON: In SIMON, analysis is performed using an intuitive graphical user interface and standardized, automated machine learning approach allowing non-technical researchers to identify patterns and extract knowledge from high-dimensional data and build thousands of high-quality predictive models using 180+ machine learning algorithms. With an easy-to-use graphical user interface, standardized pipelines, and automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.
If you plan to use machine learning to identify patterns in your data and want to learn more about SIMON and how to use it, you are all invited to join this Big Data Institute training session.
- Installation instructions: SIMON repository
- Step-by-step analysis instructions:SIMON manuscript
- Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
All course materials will be available from https://github.com/atomiclaboratory/SIMON_training_course_BDI.
Part 1 - SIMON, pattern recognition and knowledge extraction platform (May 5th 2021)
- Theoretical part
- Machine learning and AI – what is all the fuss about?
- What is SIMON?
- Case study – example 1 - FluPRINT project
Dealing with missing values, overfitting and evaluation of model performance
Part 2 - Exploratory analysis using SIMON hands-on session (May 12th 2021)
- Practical part
- Perform SIMON analysis using provided dataset
- Performance metrics, evaluation and selection of high-quality models
- Feature selection