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. The multi-omics spatial and single-cell modalities from perturbed and regenerating livers form the ideal data stack to pioneer such methods. Predictions coming from these methods will be directly validated
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, that combines diffusion and transformer models, there are clear indications that the analysis of this data can be automated. This will open new avenues in data interpretation and building predictive models
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, prognosis and therapy response prediction of cancer patients. Liquid biopsies are now offering a great potential for minimally-invasive exploration of circulating tumor nucleic acids and cells. However, some
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the fundamental aspects of transcriptional control, this project also opens new avenues for the design of climate-resilient crops. Supported by single-cell profiling and predictive artificial intelligence models
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training data. You will unravel the cis-regulatory code controlling context-dependent gene expression and use this information to design synthetic promoters. You will train and evaluate predictive models in
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disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have
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, and predict their impact on species-specific properties of human neurons. This highly multidisciplinary project will be undertaken in active collaboration with our two labs, at a unique interface of top
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to biological data sets such as omics data, protein structure prediction, or biomedical imaging. Technical experience in programming (Python preferred), and/or machine learning is a plus—not a requirement. We
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of the TOBI team is to enhance precision cancer management by developing innovative wet-lab and bioinformatic tools for diagnostic, prognostic, and predictive analysis. Prof. Katleen De Preter is the principal