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Found 4 papers, 2 papers with code
RITA: a Study on Scaling Up Generative Protein Sequence Models
In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1. 2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database.
A generative nonparametric Bayesian model for whole genomes
Generative probabilistic modeling of biological sequences has widespread existing and potential use across biology and biomedicine, particularly given advances in high-throughput sequencing, synthesis and editing.
Learning Protein Structure with a Differentiable Simulator
This gap between the expressive capabilities and sampling practicalities of energy-based models is exemplified by the protein folding problem, since energy landscapes underlie contemporary knowledge of protein biophysics but computer simulations are often unable to fold all but the smallest proteins from first-principles.
Variational Inference for Sparse and Undirected Models
Undirected graphical models are applied in genomics, protein structure prediction, and neuroscience to identify sparse interactions that underlie discrete data.
