Aperçu des sections

  • Overview

    Artificial intelligence (AI) has permeated our lives, transforming how we live and work. Over the past few years, a rapid and disruptive acceleration of progress in AI has occurred, driven by significant advances in widespread data availability, computing power and machine learning. Remarkable strides were made in particular in the development of foundation models - AI models trained on extensive volumes of unlabelled data. Moreover, given the large amounts of omics data that are being generated and made accessible to researchers due to the drop in the cost of high-throughput technologies, analysing these complex high-volume data is not trivial, and the use of classical statistics can not explore their full potential. As such, Machine Learning (ML) and Artificial Intelligence (AI) have been recognized as key opportunity areas for ELIXIR, as evidenced by a number of ongoing activities and efforts throughout the community.


    However, beyond the technological advances, it is equally important that the individual researchers acquire the necessary knowledge and skills to fully take advantage of Machine Learning. Being aware of the challenges, opportunities and constraints that ML applications entail, is a critical aspect in ensuring high quality research in life sciences


    Recognizing this need, this week-long training will bring together experts from four ELIXIR Nodes and deliver a hands-on, high-intensity course, available for members from all ELIXIR Nodes.


    Learners will be guided across the various steps in Machine Learning, from the foundational concepts, through the deep learning and generative AI techniques, closely complemented by insights into the existing reporting (DOME Recommendations) and regulatory frameworks (EU AI Act).


    This 4-day school will involve around ten trainers/helpers from across 4 different ELIXIR nodes and 30 participants from across all ELIXIR nodes. It will be hosted in France in May 2025 (see below for venue and time).

    The registration fees (including accommodation in single rooms and meals) are 650 EUR for academics and 1000 EUR for for-profit companies.

    Audience

    This course is addressed to bioinformaticians, biostatisticians, bioanalysts, life scientists and biomedical researchers with good python programming skills and general knowledge of Machine Learning approches.

    Datasets

    Applications will be agnostic of data types and species. The hands-on datasets used in the course will be aligned with participants’ preferences wherever feasible.

    Learning outcomes

    At the end of the course, the participants should be able to:

    • understand AI approaches
    • be aware of regulations and standards of AI
    • start applying learned approaches to their own data

    Prerequisites

    Knowledge / competencies
    The level of this course is intermediate, with the following requirements:

    • experience with data analysis
    • Intermediate Python programming
    • Machine Learning basics

    Technical
    You are required to bring your own laptop, more instructions will be communicated to the course participants.

    Preliminary schedule

    (detailed agenda to be circulated closer to the date)

    D1 - Monday 19 May
    Afternoon: Introduction to Machine Learning
    D2 - Tuesday 20 May
    Morning: Introduction to Neural Networks
    Afternoon:  More Neural Networks
    D3 - Wednesday 21 May
    Morning: Introduction to Deep Learning
    Afternoon: More Deep Learning
    D4 - Thursday 22 May
    Morning: Introduction to Generative AI and Large Language Models
    Afternoon: Regulation/standards for AI
    D5 - Friday 23 May
    Morning: Bring Your Own Data

    Applications

    Applications are now open at https://framaforms.org/ai-ml-in-life-sciences-application-form-1734346608. The number of participants will be limited to 30. Applications will be evaluated based on prerequisite skills and knowledge, as well as motivation for the training. 

    We will need to start the validation process late January. Thank you for your understanding. Here is provisional agenda:

    • December 18, 2024: Call for Application opens

    • January 31, 2025: Call for Application closes

    • February 21, 2025: Successful applicants announced

    • May 19, 2025: School starts


    Venue and Time

    This course will take place in the Centre de Vacances et Colloques Paul Langevin (CNRS), in the village of Aussois (Savoie, France).

    The event will start on Monday May 19th 2025 after lunch and end on Friday May 23rd 2025 around lunch time.

    Additional information

    Coordination: Lucie Khamvongsa-Charbonnier (IFB, ELIXIR-FR), Olivier Sand (IFB, ELIXIR-FR)

    Scientific committee:  Bérénice Batut (IFB, ELIXIR-FR), Alexander Botzki (VIB, ELIXIR-BE), Robbin Bouwmeester (VIB, ELIXIR-BE), Wandrille Duchemin (SIB, ELIXIR-CH), Styliani-Christina Fragkouli (CERTH, ELIXIR-GR)Ralf Gabriels (VIB, ELIXIR-BE), Raphaël Mourad (University of Toulouse III, ELIXIR-FR), Fotis Psomopoulos (CERTH, ELIXIR-GR), Harikrishnan Ramadasan (VIB, ELIXIR-BE), Thuong Van Du Tran (SIB, ELIXIR-CH)

    IFB abides by the ELIXIR Code of Conduct. Participants of IFB courses are also required to abide by the same code. Please, make sure that you read it before the event.

    For more information, please contact 

    contact-formation@groupes.france-bioinformatique.fr

  • Schedule



    HoursTitle Main instructor/presenter Support
     Day 1
       
    14:00 - 14:10 Welcome Olivier Sand Slides
    14:10 - 14:20 Icebreaking activity Bérénice Batut
     Cards
    14:20 - 14:50 Participant presentations  Slides
    14:50 - 18:50  Intro to ML 
     Wandrille Duchemin
    14:50 - 15:50 General sklearn syntax
     
    15:50 - 16:10  Coffee break 
    16:10 - 16:55  Overfit/underfit, the need for regularization,
     cross validation and a test set
      
    16:55 - 17:40 Hands-on: overfit/underfit, the need for regularization,
     cross validation and a test set
     Tutorial
    17:40 - 17:50 Short break 
    17:50 - 18:20  Metrics and imbalance  
    18:20 - 18:50  Hands-on: Metrics and imbalance  
    19:30 Dinner  
     Day 2
    09:00 - 09:15 Recap from day 1 Wandrille Duchemin 
    09:15 - 09:30  ELIXIR Fotis Psomopoulos 
    09:30 - 12:30 Intro to Neural networks Ralf Gabriels & Hari Ramadasan 
    09:30 - 10:30 Logistic regression (in pytorch)
     on a classification task
      
    10:30 - 11:00 Coffee break  
    11:00 - 12:30 Neural networks (in pytorch) on a
     classification task
      
    12:30 - 14:00 Lunch  
    14:00 - 17:30 Intro to DL (without GAI)  
    14:00 - 16:00 CNN (in pytorch) on a classification  
    16:00 - 16:30 Coffee break   
    16:30 - 17:30 RNN with attention (in pytorch)
    on a regression task
      
    19h30 Dinner  
     Day 3   
    09:00 - 09:15 Recap from day 2 Ralf Gabriels & Hari Ramadasan 
    09:15 - 12:30 Intro to GAI and LLM Raphaël  Mourad
     
    09:15 - 10:30 Introduction to LLM  
    10:30 - 11:00 Coffee break  
    11:00 - 11:45  Pretraining LLM for DNA - hands on Tutorial
    11:45 - 12:30 Hands-on: Finetuning LLM,
     zeroshot prediction for DNA variants, 
     synthetic DNA sequence generation
     Tutorial
    Tutorial
    Tutorial
    12:30 - 14:00 Lunch  
    14:00 - 17:30 Regulations/standards for AI - DOME Fotis Psomopoulos Tutorial
    14:00 - 14:30 Introduction to common challenges
     in current AI/ML applications
      
    14:30 - 15:00 Hands-on: group activity;
     review selected articles
     and assess the (potential) issues - if any
      
    15:00 - 15:15 Introduction to the DOME
     recommendations
      
    15:15 - 16:00 Hands-on:
     annotation of the previously selected articles 
     - assessment of the FAIR aspects - part 1
      
    16:00 - 16:30 Coffee break  
    16:30 - 17:00 Hands-on:
     annotation of the previously selected articles
     - assessment of the FAIR aspects - part 2
      
    17:00 - 17:30 Review of the key points of the AI Act
     and the GenAI guidelines
      
    18:45 Savoyard Apéritif  
    19:30 Savoyard Dinner  
     Day 4   
    09:30 - 12:30 General recap / Supplementary exercises All 
    09:30 - 10:20 Extra ML  
    10:20 - 10:50 Coffee break  
    10:50 - 11:40 Neural networks (in pytorch lightning) 
      
    11:40 - 12:30 Extra LLM  
    12h30 - 14:00 Lunch  
    14:00 - 17:00 Bring Your Own Question/Data/Model All 
    14:00 - 16:00 Group work  
    16:00 - 16:30 Coffee break  
    16:30 - 18:30 Group picture / Walk or work?  
    19:30 Dinner  
     Day 5   
    09:00 - 10:30 Group work  
    10:30 - 11:00 Coffee break  
    11:00 - 12:00 Group work / presentations  
    12:00 Lunch