Available courses

Summer School in Metabolic Modeling 2024

The course will introduce state-of-the-art metabolic modelling covering the full process from model reconstruction to phenotype predictions using those models. The course will focus on user-friendly tools for metabolic model reconstruction and simulation and experimental procedures to improve those models.

The course will have a five-day duration, each day divided between morning sessions with theory and afternoon sessions with practical work. The morning lectures will be guided by an expert in the field that will present background information, key concepts, computational tools, and experimental methods. The morning lecture modules will be followed by a meet the speaker time during the coffee break or lunch. Afternoon sessions will be dedicated to extensive practical work in wet and/or dry lab.

The course faculty have expertise in metabolic model reconstruction, constrained based modelling, metabolomics and experimental procedures to improve metabolic models prediction capabilities.


Artificial Intelligence for the Life Sciences

Introduction

The AILS (Artificial Intelligence for the Life Sciences) course is an exciting opportunity to explore the cutting-edge applications of machine learning and deep learning in the field of life sciences. The AILS course offers an excellent opportunity for participants to gain practical skills in artificial intelligence techniques applied to the life sciences. With a focus on hands-on learning and accessible content, this course promises to equip participants with the necessary tools to tackle real-world challenges in their respective fields. The course will take place in Braga from September 4th to 9th, 2023. It is jointly organized by OmniumAI and NEBIUM - Núcleo de Estudos de Bioinformática da Universidade do Minho.


Target Audience

AILS is accessible to non-programmers who have some experience with Python scripts and Jupyter notebooks, while a short introduction to Python programming and relevant packages (e.g., pandas) will be provided prior to the course (online session). Participants are encouraged to bring their own data, allowing them to apply the learned techniques to real-world problems they face in their respective fields.


Learning Outcomes

The course aims to provide participants with state-of-the-art knowledge and hands-on experience in artificial intelligence applied to the life sciences. The course will address the following topics:

  • Basic data manipulation and analysis in Python
  • Machine learning core concepts and tools
  • Basic bioinformatics concepts and tools
  • Basic omics data processing and analysis
  • Machine learning applied to biological sequences and omics data to predict phenotypes
  • Basic cheminformatics concepts and tools
  • Machine learning applied to chemical compounds to predict properties and activity
  • Deep learning core concepts and tools
  • Deep learning models for biological and biomedical applications
  • Generative deep learning models to generate compounds

Throughout the course, participants will work with a variety of tools and libraries, including Python scripts and Jupyter notebooks. Participants will also be introduced to the several Python libraries, essential to analyze life sciences data and to develop machine learning and deep learning models.


Practical Applications

The practical applications of the course are vast, including phenotype prediction, biomarker discovery, protein classification, prediction of compound activities and properties, and the generation of novel compounds.