Search Results for author:
Found 9 papers, 7 papers with code
PoET: A generative model of protein families as sequences-of-sequences
Generative protein language models are a natural way to design new proteins with desired functions.
Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE
Here, we consider the problem of learning semantic representations of objects that are invariant to pose and location in a fully unsupervised manner.
Antibody Representation Learning for Drug Discovery
Therapeutic antibody development has become an increasingly popular approach for drug development.
Few Shot Protein Generation
We present the MSA-to-protein transformer, a generative model of protein sequences conditioned on protein families represented by multiple sequence alignments (MSAs).
Learning to automate cryo-electron microscopy data collection with Ptolemy
Automating this is non-trivial: the images suffer from low signal-to-noise ratio and are affected by a range of experimental parameters that can differ for each collection session.
Explicitly disentangling image content from translation and rotation with spatial-VAE
Given an image dataset, we are often interested in finding data generative factors that encode semantic content independently from pose variables such as rotation and translation.
Reconstructing continuous distributions of 3D protein structure from cryo-EM images
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution.
Learning protein sequence embeddings using information from structure
We introduce a framework that maps any protein sequence to a sequence of vector embeddings --- one per amino acid position --- that encode structural information.
Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs
Cryo-electron microscopy (cryoEM) is an increasingly popular method for protein structure determination.
