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Found 4 papers, 3 papers with code
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Flow-based models allow for learning these dynamics at the population level - they model the evolution of the entire distribution of samples.
Splicing Up Your Predictions with RNA Contrastive Learning
In the face of rapidly accumulating genomic data, our understanding of the RNA regulatory code remains incomplete.
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
In this paper we leverage the fact that it is possible to estimate the "velocity" of gene expression with RNA velocity techniques to develop an approach that addresses both challenges.
Generating and designing DNA with deep generative models
We propose generative neural network methods to generate DNA sequences and tune them to have desired properties.
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