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Found 6 papers, 1 papers with code
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN.
Modeling Dynamics of Biological Systems with Deep Generative Neural Networks
DyMoN is well-suited to the idiosyncrasies of biological data, including noise, sparsity, and the lack of longitudinal measurements in many types of systems.
Harmonic Alignment
We use this to relate the diffusion coordinates of each dataset through our assumption of partial feature correspondence.
Compressed Diffusion
Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions.
Visualizing the PHATE of Neural Networks
We demonstrate M-PHATE with two vignettes: continual learning and generalization.
SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching
Translating the relevance of preclinical models ($\textit{in vitro}$, animal models, or organoids) to their relevance in humans presents an important challenge during drug development.
