Organisation profile
Organisation profile
Welcome to our innovative research group dedicated to exploring the fascinating intersection of protein structure, immunoinformatics, and computer vision tools. Our group is at the forefront of scientific discovery, employing cutting-edge technology and multidisciplinary expertise to unravel the complexities of protein patterns involved in the immune response.
Who We Are: We are a passionate group of researchers committed to unraveling protein patterns that distinguish the immune response between self and foreign. In particular, we strive to understand why a subset of foreign proteins trigger allergy in individuals expressing a handle of HLA risk alleles. In a related context, we aim to understand immune tolerance and how it is broken in autoimmune diseases. By using proteomics, protein structural information, and immunopeptidomics data to gain unprecedented insights into the world of antigen processing and presentation that led to immune recognition. Our unique approach integrates advanced computational methods and immunology allowing us to delve deep into the intricacies of proteins patterns and predict the immune response.
What We Do: At our core, we employ and develop state-of-the-art clsutering, machine learning and computer vision tools to predict which protein features are involved in abnormal immune activation. Our focus on immunoinformatics enables us to explore the immunological aspects of proteins, unraveling their roles in health and disease.
Our Mission: Our mission is to advance the frontiers of knowledge on how the immune tolerance is developed and when this fails in disease. By harnessing the potential of deep learning and computer vision, we aim to decode the complexity of the immune response, paving the way for groundbreaking discoveries in the ethiology allergy and autoimmunity. We leverage computer vision tools to visualize and interpret intricate protein structures, facilitating a deeper understanding of their roles in biological processes. We are dedicated to fostering collaboration, encouraging curiosity, and nurturing the next generation of scientists.
Areas of Expertise:
- Allergenicity prediction: We specialize in learning protein features and HLA restriction patterns to predict if a protein can trigger allergy.
- Antigen processing and immunopeptidomics: Our expertise in machine learning techniques allows us to develop advanced algorithms for predicting protein structure and their involvement in immune processing which is at the core of the immune response.
- Autoimmune diseases: By learning protein patterns in well studied autoimmune diseases, we aim to predict which features are key for breaking the tolerance and aim to predict protein targets involved in rare autoimmune diseases.
The core research of the group deals with the development of novel and advanced data-driven prediction methods for pattern recognition in immunoinformatics.
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Collaborations and top research areas from the last five years
Profiles
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Carolina Barra Quaglia
- Department of Health Technology - Associate Professor
- Bioinformatics
- Protein Immunoinformatics
Person: VIP
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Autoimmune associated HLAs and T cell autoantigens exhibit common patterns across several autoimmune diseases
Saksager, A. B., Hede, F. D. & Barra, C., 2025, In: Journal of Autoimmunity. 155, 19 p., 103443.Research output: Contribution to journal › Journal article › Research › peer-review
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Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers
Lorenzen, N. R., Jennum, P. J., Mignot, E. & Brink-Kjaer, A., 2025, Proceedings of the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) . IEEE, Vol. 2025. 7 p. (Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Self-Supervised Pretraining for Wrist-Worn Accelerometer Data Improves Activity and Sleep-Wake Discrimination
Lorenzen, N., Mignot, E., Brink-Kjaer, A. & Jennum, P., 2025, In: Sleep. 48, Suppl. 1, p. A194-A194Research output: Contribution to journal › Conference abstract in journal › Research › peer-review
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AI Oracles for Fitness Landscape Optimization on Molecular Binders
Romero Yianni, D. C. (PhD Student), Quaglia, C. B. (Main Supervisor), Papaleo, E. (Supervisor), Ferkinghoff-Borg, J. (Supervisor) & Marcatili, P. (Supervisor)
15/05/2025 → 14/05/2028
Project: PhD
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Harnessing AI to Identify Superior Strains for Plant-Based Starter Cultures
Nielsen, M. R. (PhD Student), Bang-Berthelsen, C. H. (Main Supervisor), Quaglia, C. B. (Supervisor), Hansen, E. B. (Supervisor) & Karlsen, S. T. (Supervisor)
01/02/2025 → 31/01/2028
Project: PhD
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Explainable AI and Bioinformatics for Health
Papagoras, K. A. (PhD Student), Lund, O. (Main Supervisor), Quaglia, C. B. (Supervisor) & Ebrahimi, P. (Supervisor)
01/01/2025 → 31/12/2027
Project: PhD