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of 58
pro vyhledávání: '"Gligorijevic, Vladimir"'
Across scientific domains, generating new models or optimizing existing ones while meeting specific criteria is crucial. Traditional machine learning frameworks for guided design use a generative model and a surrogate model (discriminator), requiring
Externí odkaz:
http://arxiv.org/abs/2405.18075
Autor:
Martinkus, Karolis, Ludwiczak, Jan, Cho, Kyunghyun, Liang, Wei-Ching, Lafrance-Vanasse, Julien, Hotzel, Isidro, Rajpal, Arvind, Wu, Yan, Bonneau, Richard, Gligorijevic, Vladimir, Loukas, Andreas
We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new representation of protein structure, relies on a novel architecture for
Externí odkaz:
http://arxiv.org/abs/2308.05027
In silico screening uses predictive models to select a batch of compounds with favorable properties from a library for experimental validation. Unlike conventional learning paradigms, success in this context is measured by the performance of the pred
Externí odkaz:
http://arxiv.org/abs/2307.09379
Autor:
Frey, Nathan C., Berenberg, Daniel, Zadorozhny, Karina, Kleinhenz, Joseph, Lafrance-Vanasse, Julien, Hotzel, Isidro, Wu, Yan, Ra, Stephen, Bonneau, Richard, Cho, Kyunghyun, Loukas, Andreas, Gligorijevic, Vladimir, Saremi, Saeed
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 man
Externí odkaz:
http://arxiv.org/abs/2306.12360
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Autor:
Tagasovska, Nataša, Frey, Nathan C., Loukas, Andreas, Hötzel, Isidro, Lafrance-Vanasse, Julien, Kelly, Ryan Lewis, Wu, Yan, Rajpal, Arvind, Bonneau, Richard, Cho, Kyunghyun, Ra, Stephen, Gligorijević, Vladimir
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences. However, these problems often require sampling new designs that satisfy multiple properties of interest in addition to
Externí odkaz:
http://arxiv.org/abs/2210.10838
Autor:
Park, Ji Won, Stanton, Samuel, Saremi, Saeed, Watkins, Andrew, Dwyer, Henri, Gligorijevic, Vladimir, Bonneau, Richard, Ra, Stephen, Cho, Kyunghyun
Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences. Whereas it is possible to optimize the various properties of interest jointly using a mu
Externí odkaz:
http://arxiv.org/abs/2210.04096
Autor:
Berenberg, Daniel, Lee, Jae Hyeon, Kelow, Simon, Park, Ji Won, Watkins, Andrew, Gligorijević, Vladimir, Bonneau, Richard, Ra, Stephen, Cho, Kyunghyun
Deep generative modeling for biological sequences presents a unique challenge in reconciling the bias-variance trade-off between explicit biological insight and model flexibility. The deep manifold sampler was recently proposed as a means to iterativ
Externí odkaz:
http://arxiv.org/abs/2205.04259
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Networks have been a general tool for representing, analyzing, and modeling relational data arising in several domains. One of the most important aspect of network analysis is community detection or network clustering. Until recently, the major focus
Externí odkaz:
http://arxiv.org/abs/1612.00750
Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved path
Externí odkaz:
http://arxiv.org/abs/1410.7585