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Found 4 papers, 2 papers with code
Deep learning models for predicting RNA degradation via dual crowdsourcing
Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models.
Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation
We thus recommend leveraging the autocorrelation of the latent space while training neural network models of biomolecular simulation data to better represent slow processes.
Transferable neural networks for enhanced sampling of protein dynamics
In this work, we illustrate how this non-linear latent embedding can be used as a collective variable for enhanced sampling, and present a simple modification that allows us to rapidly perform sampling in multiple related systems.
Variational Encoding of Complex Dynamics
Recent work in the field of deep learning has led to the development of variational autoencoders (VAE), which are able to compress complex datasets into simpler manifolds.
