END-nSDE reconstructs SDEs from heterogeneous cell trajectories via Wasserstein distance, applied to circadian rhythms, RPA-DNA binding, and NFκB signaling while outperforming RNNs and LSTMs.
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Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations
END-nSDE reconstructs SDEs from heterogeneous cell trajectories via Wasserstein distance, applied to circadian rhythms, RPA-DNA binding, and NFκB signaling while outperforming RNNs and LSTMs.