SOURCE-LINKED INTELLIGENCE

Wasserstein FLOW Learning for multi-Omics

CORDIS · observation · Publication date unknown

grated in an efficient computational package where the parameters of the models are learned using parallelizable OT flow solvers. Leveraging the connexion between OT flows and attention mechanisms in deep learning, these methods will be approximated using transformers architectures and optimized using implicit differentiation. These theoretical and numerical contributions will work hand in hand to offer the first comprehensive framework for multi-omics trajectory inference. This will unlock biological findings for the characterization of developmental molecular pathways and the understanding of disease mechanisms." Optimal transport, Deep learning, Transformers, Genomics, Trajectory inference

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recordType

award

status

SIGNED

region

EU

value

2500000

unit

EUR