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Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data
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personalized immunotherapies for cancer patients with brain metastasis. Our group works at the interface between immunology and cancer biology combining the use of preclinical models of cancer and human samples
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-trained tool on other image sensors and at different mountain sites presents difficulties. Specifically, generalizing a model trained on a specific site to other sites with different environmental
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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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for personalized treatment or as novel actionable therapeutic targets. We focus on gastric cancer, for which we have cell lines, patient-derived xenografts, a syngeneic tumor model, and comprehensive patient tissue
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structurally and functionally characterizing macromolecular complexes allowing the initiation of translation initiation of translated model messenger RNAs in Neurodegenerative Disease patients. The approaches
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retinal degeneration in the context of ciliopathies, with a particular focus on Alström syndrome, using both in vivo models (particularly murine models) and in vitro systems (fibroblasts, iPSCs, retinal
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. - Conduct high-throughput serum proteomic analyses and integrate molecular datasets. - Validate candidate biomarkers in independent cohorts. WP3.2 – Integrated predictive modeling: - Develop integrative multi
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. The work will be primarily computational, focusing on the development of deep neural network model architectures and their training. It will involve extending the preliminary results we have already obtained