Introduction to AI and Machine Learning for Biomedical Research
Join us for the second edition of this successful introductory training course on AI for Biomedical research. Get familiarised with AI models and state-of-the-art biomedical research based on machine learning.
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- Register until: 06 Nov 2023
- Price: Free
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Huge amounts of data are available today for biomedical research. These data arise in different forms like images, omics-data, electronic medical files, etc. Machine Learning algorithms can help researchers to find patterns, classify the data or help to make well-founded decisions, based on these data. Typical examples are the analysis of CT scans for cancer diagnoses or the identification of the proper treatment for MS patients.
Programme
Module 1: Basics of Machine Learning & Supervised Learning >>>
Jef Vandemeulebroucke - VUB
6 November 2023 - 13-16h
Module 2: Unsupervised Learning >>>
Celine Vens - KU Leuven
10 November 2023 - 13-15h
Module 3: Deep Learning and Neural Networks >>>
Joris Roels - Radix
13 November 2023 - 13-15h
Module 4: Reinforcement Learning >>>
Pieter Libin - VUB
16 November 2023 - 13-15h
Module 5: Use cases from the biomedical sector - Part 1: Research >>>
Module 6: Use cases from the biomedical sector - Part 2: Industry >>>
Module 7: Fairness and Privacy of AI >>>
Learning outcomes
- Become familiarised with the basic concepts of machine learning to better convey your biomedical research problem to an AI expert
- Learn how to apply AI in your research
- Gain insight into current biomedical research topics that apply these techniques
- Get an overview of relevant use cases and get in touch with experts from the industry
- Learn about some important concerns/ethical issues when applying these AI techniques.
Programme
Module 1: Basics of Machine Learning & Supervised Learning
Jef Vandemeulebroucke - VUB
6 November 2023 - 13h-16h
Module 2: Unsupervised Learning
Celine Vens - KU Leuven
10 November 2023 - 13h-15h
In unsupervised learning, we aim to analyse and learn from unlabeled datasets. The algorithms discover hidden patterns or data groups without the need for human supervision. In this module, we will discuss several tasks and techniques of unsupervised learning, including clustering, association rule mining, anomaly detection and recommender systems. We will also touch upon semi-supervised learning. For each task, we will discuss some examples from the biomedical domain.
Module 3: Deep Learning and Neural Networks
Joris Roels - Radix
13 November 2023 - 13h-15h
In this module, we will elaborate on neural networks and deep learning, with a specific focus on the biomedical research community. We will delve into the intricate workings of neural networks, various derived architectures (CNNs, RNNs, Transformers, etc.), and more advanced generative AI algorithms. Furthermore, we will elucidate how these technologies are reshaping the landscape of biomedical research. From screening and diagnosing in medical imaging to drug discovery and protein folding, deep learning is revolutionizing the way we approach complex problems in biomedical research.
Module 4: Reinforcement Learning
Pieter Libin - VUB
16 November 2023 - 13h-15h
Reinforcement learning concerns a distinct branch in the field of machine learning. While supervised and unsupervised machine learning techniques learn based on a set of instances, reinforcement learning concerns an agent that learns by interacting with an environment. To illustrate this setting, consider a video game, where the agent identifies as the player of the game. The environment concerns the video game that can be controlled with a gamepad and observed by looking at the screen. The agent receives feedback from the environment in terms of rewards, for example via a game score. Based on this feedback and the observations of the environment, a reinforcement learning agent aims at learning to play the game optimally, by interacting repeatedly with the environment. In this course, we will study both the reinforcement learning problem and the algorithms that can be used to approach such problems.
Over the last years, several important milestones were achieved using the reinforcement learning framework, such as learning to play ATARI 2600 video games, learning to play the board game Go to beat the number one human player and learning to reach grandmaster performance in the real-time strategy game StarCraft II. However, reinforcement learning is no longer limited to games, as many real-world problems can be formulated as a reinforcement learning problem. Recent studies used reinforcement learning to solve challenging problems such as wind farm control, epidemic mitigation, and robot control.
Module 5: Use cases from the biomedical sector - Part 1: Research
27 November 2023 - 13h-14h40
- Liesbet Peeters (UHasselt): Artificial intelligence for real-world-evidence generation
- Pieter Libin (AI Lab VUB): Deep Reinforcement Learning for Epidemic Policy Control
- Sven Degroeve (UGent - VIB): Machine Learning predictions of peptide behaviour for improved identification of modified peptides
- Danny Volkaerts (UCLL): Integration of physical activity in type 1 diabetes management: the smart diabetes assistant
Module 6: Use cases from the biomedical sector - Part 2: Industry
30 November 2023 - 13h-14h40
- Nikolay Manyakov (Janssen Pharmaceutical Company): Data science applications in clinical trials
- Brecht Coghe (Radix): Detecting Early Onset Sepsis in the Neonatal Intensive Care Unit with Machine Learning
- Yin Cai (AWS): Machine Learning & Generative AI applications in Healthcare
- Alexander Koch (BioLizard): Designing antimicrobial peptides
Module 7: Fairness and Privacy of AI
Maarten Buyl - UGent
7 December 2023 - 13h-15h
Through machine learning, AI systems can learn and reproduce patterns of human intuition in datasets on a large scale. However, human behaviour in high-impact decision-making has always come with ethical codes and expectations, especially in biomedical sciences. Now that AI systems play an increasingly substantial role in such decisions, they should also aim to meet ethical standards. In this module, we particularly consider the ethical principles of fairness and privacy in AI systems, both highly active fields of research. Fairness aims to avoid statistical patterns of discrimination in decision-making, such as performing far worse for certain demographic groups. Privacy aims to avoid revealing sensitive information, such as people's medical records. We will discuss synergies between these two principles, but also their conflicts.
Teachers / speakers
Jef Vandemeulebroucke
Expertise: medische beeldanalyse, computer-ondersteunde diagnose and beeldgestuurde interventies
Celine Vens
Celine Vens is professor at KU Leuven campus Kulak and leading the machine learning and AI subgroup at itec. Her main research interest is the development of machine learning algorithms for applications in health and education. She focuses on non-standard supervised and semi-supervised learning tasks, such as multi-output prediction, time-to-event prediction and interaction prediction. Besides research, she is teaching several courses at the Faculty of Medicine.
Pieter Libin
Prof. Pieter Libin graduated in 2014 at Vrije Universiteit Brussel in Informatics and obtained his PhD in Computer Science at VUB in 2020. After his PhD, he held a postdoctoral position funded by the Flemish science foundation at Hasselt university, at the department of data science. Since October 2021, Pieter is active as an assistant professor at the AI lab of the Vrije Universiteit Brussel. His research involves the use of machine learning to support decision makers by combining machine learning techniques with realistic simulation models and concerns theoretical and applicational AI with a focus on reinforcement learning and Bayesian modeling. He has a broad experience in modeling and analyzing real-world systems ranging from virus diversity, epidemic emergencies, and renewable energy providers. Pieter is a member of the Jonge Academie, an association of young top researchers and artists with an engagement to policy, society, research, and the arts. Pieter is a board member of the Benelux Association for Artificial Intelligence.
Sven Degroeve
Sven Degroeve is a seasoned expert in machine learning and deep learning applications. He is a senior computer scientist, highly proficient in implementing AI solutions. Beginning his career in 1999 at Flanders Language Valley, he has developed advanced AI models for gene prediction and virus mutation analysis. After joining UGent and biotech start-up Pronota, he leveraged his expertise for biomarker identification and industry operations. Currently, as a VIB staff scientist and ZAP position holder at Ghent University, he specializes in high-throughput LC-MS data analysis. He participates in major EU-funded projects and teaches popular university courses and private workshops on AI and machine learning.
Danny Volkaerts
Expertise: Data visualisatie, SQL, Data Analyse, Data Analytics, Machine Learning, AI
Alexander Koch
I'm Alexander and I'm a data science team lead at BioLizard. Our goal is to transform the biomedical data of our clients into actionable insights. Using data mining, data visualisation, bioinformatics pipeline development, software development, and the construction of ML and AI models, we support biotech and pharmaceutical companies and research institutes in their (pre)clinical research and development efforts.
Maarten Buyl
Postdoctoral researcher at Ghent University
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