Search Results for author: Stephen Ra

Found 10 papers, 4 papers with code

Blind Biological Sequence Denoising with Self-Supervised Set Learning

no code implementations4 Sep 2023 Nathan Ng, Ji Won Park, Jae Hyeon Lee, Ryan Lewis Kelly, Stephen Ra, Kyunghyun Cho

This set embedding represents the "average" of the subreads and can be decoded into a prediction of the clean sequence.


OpenProteinSet: Training data for structural biology at scale

1 code implementation NeurIPS 2023 Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Lukas Jarosch, Daniel Berenberg, Ian Fisk, Andrew M. Watkins, Stephen Ra, Richard Bonneau, Mohammed AlQuraishi

Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades.

Protein Design Protein Structure Prediction

Protein Discovery with Discrete Walk-Jump Sampling

1 code implementation8 Jun 2023 Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hotzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi

We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising.


BOtied: Multi-objective Bayesian optimization with tied multivariate ranks

no code implementations1 Jun 2023 Ji Won Park, Nataša Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho

At the heart of MOBO is the acquisition function, which determines the next candidate to evaluate by navigating the best compromises among the objectives.

Bayesian Optimization

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling

1 code implementation7 Nov 2022 Romain Lopez, Nataša Tagasovska, Stephen Ra, Kyunghyn Cho, Jonathan K. Pritchard, Aviv Regev

Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic.

Disentanglement Domain Generalization +1

Multi-segment preserving sampling for deep manifold sampler

no code implementations9 May 2022 Daniel Berenberg, Jae Hyeon Lee, Simon Kelow, Ji Won Park, Andrew Watkins, Vladimir Gligorijević, Richard Bonneau, Stephen Ra, Kyunghyun Cho

We introduce an alternative approach to this guided sampling procedure, multi-segment preserving sampling, that enables the direct inclusion of domain-specific knowledge by designating preserved and non-preserved segments along the input sequence, thereby restricting variation to only select regions.

Language Modelling

Black Box Recursive Translations for Molecular Optimization

no code implementations21 Dec 2019 Farhan Damani, Vishnu Sresht, Stephen Ra

We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization.

 Ranked #1 on Molecular Graph Generation on ZINC (QED Top-3 metric)

Drug Discovery Molecular Graph Generation +1

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