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. We develop machine learning methods tailored for high-dimensional, multimodal biological data, with applications ranging from single-cell genomics to real-world clinical datasets. We are active
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Kim (DANDRITE) This project explores how mutations in chromatin-modifying genes disrupt early neurodevelopment by systematically profiling in vitro neurons using multimodal single-cell and spatial
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at the Faculty of Medicine, University of Helsinki. The project will focus on using and extending deep learning-based approaches developed within the group to integrate bulk multi-omics cancer data. The Kuijjer
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regulatory interactions, (ii) modeling networks based on single-cell and spatial omics data, and (iii) integrating regulatory profiles with multi-modal data. These efforts aim to uncover regulatory mechanisms