Search Results for author:
Found 17 papers, 11 papers with code
Dirichlet Flow Matching with Applications to DNA Sequence Design
Further, we provide distilled Dirichlet flow matching, which enables one-step sequence generation with minimal performance hits, resulting in $O(L)$ speedups compared to autoregressive models.
AlphaFold Meets Flow Matching for Generating Protein Ensembles
When trained and evaluated on the PDB, our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling.
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms
The runtime of our approach can be amortized at several levels of abstraction, and is particularly favorable for virtual screening settings with a common binding pocket.
EigenFold: Generative Protein Structure Prediction with Diffusion Models
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function.
Causally-guided Regularization of Graph Attention Improves Generalizability
Graph attention networks estimate the relational importance of node neighbors to aggregate relevant information over local neighborhoods for a prediction task.
Granger causal inference on DAGs identifies genomic loci regulating transcription
When a dynamical system can be modeled as a sequence of observations, Granger causality is a powerful approach for detecting predictive interactions between its variables.
Enriching and Characterizing T-Cell Repertoires from 3' Barcoded Single-Cell Whole Transcriptome Amplification Products
In short, a fraction of the 3' barcoded whole transcriptome amplification (WTA) product typically used to generate a massively parallel 3' scRNA-seq library is enriched for TCR transcripts using biotinylated probes, and further amplified using the same universal primer sequence from WTA.
Exploring generative atomic models in cryo-EM reconstruction
Although reconstruction algorithms typically model the 3D volume as a generic function parameterized as a voxel array or neural network, the underlying atomic structure of the protein of interest places well-defined physical constraints on the reconstructed structure.
Multi-resolution modeling of a discrete stochastic process identifies cusses of cancer
Detection of cancer-causing mutations within the vast and mostly unexploredhuman genome is a major challenge.
CryoDRGN2: Ab Initio Neural Reconstruction of 3D Protein Structures From Real Cryo-EM Images
In this work we describe cryoDRGN2, an ab initio reconstruction algorithm, which can jointly estimate image poses and learn a neural model of a distribution of 3D structures on real heterogeneous cryo-EM data.
Learning Mutational Semantics
In many natural domains, changing a small part of an entity can transform its semantics; for example, a single word change can alter the meaning of a sentence, or a single amino acid change can mutate a viral protein to escape antiviral treatment or immunity.
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.
Large-Margin Classification in Hyperbolic Space
Representing data in hyperbolic space can effectively capture latent hierarchical relationships.
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.
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks
In this paper, we introduce diffusion component analysis (DCA), a framework that plugs in a diffusion model and learns a low-dimensional vector representation of each node to encode the topological properties of a network.
