The past decade has produced overwhelming evidence that changes in the health status of individuals, measured by well-defined quantitative clinical endpoints, can be predicted by computer models. This has opened the door to several applications for computer modeling and simulation technologies. The modeling approach depends on the technologies used and the quality and strength of the data and...
This lecture will give an overview about basic mechanisms and workflows that can be used to collect and prepare data for different re-use cases in the domain of chemistry. It will emphasize the importance of standardized processes and data conversion routines and will describe the role of open data for the development of new AI-based scientific methods in the long run.
Even though machine learning and data-driven models acquire more and more momentum, traditional mechanistic models are still very useful in different fields. Mechanistic models can be thought of as virtual mimics of real systems with some simplifications if necessary. Such models are very useful when the amount of data is limited, or we need to understand the causation of the made observations...
In this lecture, we will discuss approaches to learning predictable models directly from data. We will start with the fundamentals of statistical learning theory, statistical inference, and machine learning. We will then cover the basics of deep learning, a few network architectures, and current applications in biology and medicine.
This lecture explores the integration of medical digital twins in patient care, focusing on the personalised modelling of individual patients. We will discuss the necessary data sources for highly parametrised models, exemplified by the scarcity of measurements associated with personal harm to the patient and the potential role of machine learning in this problem. Practical examples of...
In this lecture, we will motivate why the successful application of machine learning models in the real world (in the context of Digital Twins or otherwise) requires a careful quantification of predictive uncertainty. We will review different fundamental approaches to uncertainty estimation, such as frequentist and Bayesian ones. We will give an overview of several practical methods for...
Single-cell genomics has enabled the construction of detailed organ atlases, offering unprecedented insights into cellular states and their perturbations by signaling, drugs, or disease. A key challenge is moving to actionable models that can predict and steer cellular responses. Therefore, we need generative models that not only organize single-cell data in meaningful manifolds but also...
Advanced surgical technologies, like digital ORs and robotics, generate vast data for enhancing patient care. However, leveraging this data efficiently during surgery, a complex and time-sensitive process, remains heavily reliant on surgical staff experience.
This lecture introduces digital twins for AI-powered robotic surgery with a particular focus on analysis of intraoperative data. By...
Computational modeling and digital twins have become state-of-the-art methodologies in all industries to analyze, monitor, and assess the possible outcomes of a system under different conditions. Specifically, in the healthcare sector, the full exploitation of computational models is on the rise. ELEM Biotech is a spinoff of the Barcelona Supercomputing Center that commercially exploits...
