Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Planning to use machine learning to better understand your data? In this interactive course, we will learn how to use machine learning for biological and biomedical data integration and knowledge discovery.

COURSE DESCRIPTION

In the Systems immunology: an intro to multi-omics data integration and machine learning course, we will learn how to perform integrative analysis using SIMON, our recently developed machine learning approach. 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. SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

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 integrative analysis. By the end of this course users should be able to perform SIMON analysis of their own data including data preparation and exploration analysis.

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

HOW IT WILL WORK

This is 2-day interactive online course comprised of theoretical and practical parts. Users will receive a link and joining instructions beforehand. Users are required to install SIMON software before the course and read the original manuscripts describing SIMON (Tomic et al, JI, 2019 and Tomic et al, Patterns, 2021). Before the first session, instructor will ensure that everyone is set up to start with the practical parts of the course. The sessions are divided into two parts: theoretical and practical, with breaks for questions and to help users to complete the practical exercises.

Course overview:

Day 1 - SIMON, pattern recognition and knowledge discovery platform for integrative analysis

  • Theoretical part: Introduction to Machine learning and AI
  • Theoretical + Practical part - dealing with missing data overfitting, model performance
  • Practical part - perform SIMON analysis using provided dataset
  • Theoretical part + results from the analysis - performance metrics, evaluation, and selection of high-quality models

Day 2 - Exploratory analysis

  • Theoretical + practical part: feature selection: scoring and elimination
    • Theoretical + practical part: correlation and clustering analysis
    • Theoretical part - feature processing methods to avoid curse of dimensionality
    • Practical part - case study using provided dataset

WHAT YOU WILL NEED

The prerequisite for this course is to install SIMON software available on the software website: https://genular.org/.

Optional:

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.

PLEASE NOTE

Where no cost is indicated in the shopping trolley, no deposit is required. However, two or more non-attendances or late cancellations without good reason will be logged and may mean you cannot attend any further MSD training that term. Please refer to our Terms and Conditions for further information.