ERC Advanced Grant · 2023
Wasserstein FLOW Learning for multi-Omics
Single cell molecular profiling allows to map cellular development at an unprecedented level of detail. Optimal transport (OT) enables the analysis of this dynamical process as a trajectory inference problem, using OT flows. These flows treat cells as particles evolving on an energy landscape over an "omics'' space (such as transcriptomic, epigenomic, proteomic and location). Learning this model from large scale omics datasets poses however formidable mathematical and computational challenges, which will be tackled by WOLF. The first one is the joint learning of both the gene embedding space and the energy landscape. Existing approaches use ad-hoc Euclidean embeddings, ignoring biological…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.
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