Cilia-AI combines the expertise of 9 academic, 2 industrial, and 2 non-profit sector partners that are all motivated to train a new, pan-European cohort of DCs to become the next generation of independent cilia researchers.
The Cilia-AI consortium is dedicated to linking genotype-to-phenotype relationships in the ciliopathies, by:
🔬 Unravel variant-function relationships in cilia biology.
🔬 Enhance molecular and cellular profiling of ciliopathy disease states.
🔬 Leverage machine learning technology to advance imaging and analysis at multiple scales.
Understanding how cilia work, both in health and in disease, requires a blend of expertise from many different fields. We use next-generation technologies like:
AI-assisted structural biology to understand the detailed structures of cilia.
Quantitative proteomics to analyze the proteins involved.
Whole exome and long-range whole genome sequencing to investigate genetic factors.
Single cell- and single nucleus, and spatial transcriptomics to study gene expression at the single-cell level.
Super-resolution microscopy and cryo-electron tomography, combined with expansion microscopy, to obtain highly detailed imaging data.
All these techniques generate large amounts of data, and integrating this information to gain meaningful insights is challenging. This is where machine learning, a key area of artificial intelligence, comes into play. Machine learning helps us process and analyze these complex datasets, integrating data from different disciplines, and extracting valuable insights for biomedical research.
