Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning
- Date: Sunday, September 18th
- Time: 14:00 to 18:00 CEST (Slot 26)
- Format: Face-to-face
- Room: TBD
- María Rodríguez-Martínez
- Anna Niarakis
- Matteo Barberis
Interpretable machine learning & statistical modelling of the immune system in health and disease settings
The immune system is highly complex, and its malfunctioning can result in a wide number of disorders. A correct understanding of its inner workings is crucial to design optimal immunotherapies, develop new vaccines, understand the molecular basis of autoimmune diseases, etc. However, immune-related diseases pose specific challenges associated with the incomplete understanding of the underlying highly non-linear molecular and cellular interactions, and with the limited therapeutic options available based on existing drugs.
Recent years have witnessed the unstoppable development of high-throughput experimental technologies in molecular biology. We can now routinely profile thousands or even millions of single cells and millions of profiles are already publicly available. The availability of large amounts of molecular data has powered the development of many statistical and machine learning models focused on understanding the complexity of the immune system. Statistical and machine learning approaches have long been considered orthogonal approaches, the former being focused on modelling statistical relationships, and the latter on identifying hidden data patterns and correlations. However, the synergy between both approaches may greatly improve the accuracy and coverage of computational models, and help to disentangle the complex mechanisms that govern immune processes.
Attention will be given to the rising field of interpretable deep learning, which aims to overcome the black-box nature of most currently available deep learning models. Lack of understanding—inherent to black-box models—is detrimental in high-stake scenarios, such as biomedical research, where patients and clinicians need to understand the causal relationships underpinning model predictions. The alternative to black-box models, mechanistic models, allow to guide precise, testable predictions; however, it may be challenging to readily apply these models to high-throughput data.
By bringing together experts working in the fields of mechanistic, statistical, and AI modelling, this workshop aims to investigate how information about the molecular mechanisms underlying biological functions and cellular processes can be extracted from high-throughput data.
The target audience ranges from students, early and advanced researchers to professionals working in the fields of Immunology, Systems Biology, Computational Biology, Computer Science and Bioinformatics who apply or are interested in applying computational modelling techniques and machine learning approaches for studying cellular functions of the immune system.
|14:00 – 14:10||Welcome and introduction to the workshop|
|Session 1 – Statistical models|
Chair: María Rodríguez Martínez
|14:10 – 14:40||Data-driven model for neo-antigen/T-cell presentation and|
recognition: the case of pancreatic cancer.
Remi Monasson (Ecole Normale Supérieure, France)
|14:40 – 15:10||Statistical and machine-learning analysis of adaptive immune|
Victor Greiff (University of Oslo, Norway)
|Session 2 – High throughput data analysis methods|
Chair: Anna Niarakis
|15:10 – 15:40||Inferring shared intratumor heterogeneity from bulk RNA-seq|
data without matched single-cell reference.
Valentina Boeva (ETH, Switzerland)
|16:00 – 16:30||Insights from topological data analysis of COVID-19 single-cell|
Davide Cirillo (Barcelona Supercomputing Center, Spain)
|Session 3 – Machine learning|
Chair: Matteo Barberis
|16:30 – 17:00||Tackling the complexity of (unseen) epitope-TCR predictions.|
Pieter Meysman (University of Antwerp, Belgium)
|17:00 – 17:20||Interpretable machine learning to unravel T cell receptor binding rules. |
Anna Weber (IBM Research Europe, Switzerland)
|Session 4 – Discussion|
Chairs: María Rodríguez Martínez, Anna Niarakis, Matteo
|17:20 – 17:50||Round table discussion with speakers and participants. Discussion on key challenges and open questions ofimmunological modelling moderated by the three organizers.|
|17:50 – 18:00||Closing remarks|