GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
Keywords: single-cell genomics, optimal transport, flow matching
TL;DR: Neural Optimal Transport Solvers based on Flow Matching for Gromov and Wasserstein OT motivated by tasks in single-cell genomics
Abstract: Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. However,
single-cell sequencing technologies are inherently destructive and can only measure a limited array of data modalities simultaneously. This limitation underscores
the need for new methods capable of realigning cells. Optimal transport (OT)
has emerged as a potent solution, but traditional discrete solvers are hampered by
scalability, privacy, and out-of-sample estimation issues. These challenges have
spurred the development of neural network-based solvers, known as neural OT
solvers, that parameterize OT maps. Yet, these models often lack the flexibility
needed for broader life science applications. To address these deficiencies, our
approach learns stochastic maps (i.e. transport plans), allows for any cost function,
relaxes mass conservation constraints and integrates quadratic solvers to tackle the
complex challenges posed by the (Fused) Gromov-Wasserstein problem. Utilizing
flow matching as a backbone, our method offers a flexible and effective framework.
We demonstrate its versatility and robustness through applications in cell development studies, cellular drug response modeling, and cross-modality cell translation,
illustrating significant potential for enhancing therapeutic strategies.
Primary Area: Machine learning for healthcare
Submission Number: 6485
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