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Integration of single-cell multi-omics data across space and time to unlock cellular trajectories

CORDIS · observation · Publication date unknown

ds will be implemented in open-source software, with an emphasis on GPU-friendly scalable computations, a unique feature among existing single-cell tools (Aim3). These core contributions will impact Machine Learning, but more importantly, will have profound biological implications. The application of the tools developed to cutting-edge single-cell data from muscle stem cells will lead to new biological hypotheses on their heterogeneity and crosstalk, to be validated through wet-lab experiments (Transversal Tasks). In addition, by allowing to answer longstanding questions on the spatiotemporal phenotypic evolution of a cell, MULTIview-CELL will catalyze the generation of crucial knowledge in fundamental biology and it will be key to preventing disease onset or therapy resistance, thus impacting health, society and economy. Multimodal Machine Learning, Optimal Transport, graphs, single-cell, multi-omics

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recordType

award

status

SIGNED

region

EU

value

1285938

unit

EUR