Project description
Training in AI-powered research to tackle cilia function in health and disease
Primary cilia are antenna-like sensory organelles at the cell surface essential for various physiological functions such as hearing, smell, and respiration. Dysfunctional cilia can lead to over 35 severe diseases known as ciliopathies, affecting about 1 in 400 people. These diseases exhibit diverse and overlapping symptoms, complicating diagnosis and treatment. Understanding the multi-level organisation and regulation of cilia in health and disease remains a significant scientific challenge. With the support of the Marie Skłodowska-Curie Actions programme, the Cilia-AI project will use advanced imaging and AI technologies to study cilia at different scales. Specifically, it will train 15 doctoral candidates in multidisciplinary biomedical research and emerging machine learning fields. The training will include individual research projects, secondments, and network-wide sessions.
Objective
Cilia-AI will train a new generation of multidisciplinary biomedical researchers and entrepreneurs, and those specializing in emerging machine learning technologies, a subset of AI. The focus is the study of primary cilia, microtubule-based projections on cell surfaces that play a pivotal role in coordinating cellular signalling pathways during development and homeostasis of cells, tissues and organs. These tiny structures are essential for various physiological functions such as hearing, smell, respiration, excretion and reproduction. Dysfunctional cilia can lead to >35 severe human diseases known as ciliopathies, exhibiting diverse and overlapping phenotypes, affecting up to 1 in 400 people. To unravel the multi-level organisation and regulation of cilia in health and disease, Cilia-AI employs a multidisciplinary approach, integrating cutting edge technologies like structural biology, omics- and organoid technologies. Advanced imaging techniques, including super-resolution microscopy, cryo-electron tomography and expansion microscopy, will be used to generate high-resolution and versatile datasets. Processing such data requires sophisticated computational methods. Cilia-AI is at the forefront of implementing and developing machine learning approaches to decipher these high-content datasets and integrate diverse multidisciplinary data. Cilia-AI offers unparalleled training opportunities for 14 Doctoral Candidates (DCs) in both academic and industrial settings. The training involves individual research projects, secondments, and network-wide sessions. This training equips DCs with skills attractive to both industrial and academic sectors, enhancing their career prospects in these domains. Overall, Cilia-AI’s research and training activities contribute to advancing the understanding of cilia in health and disease while fostering a new generation of skilled professionals with broad competences.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social scienceseconomics and businessbusiness and managemententrepreneurship
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- medical and health sciencesbasic medicinephysiologyhomeostasis
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral NetworksCoordinator
6525 GA Nijmegen
Netherlands