Article

Latent generative modeling of long genetic sequences with GANs

2024-08-07

Abstract excerpt

Synthetic data generation via generative modeling has recently become a prominent research field in genomics, with applications ranging from functional sequence design to high-quality, privacy-preserving artificial in silico genomes. Following a body of work on Artificial Genomes (AGs) created via various generative models trained with raw genomic input, we propose a conceptually different approach to address the...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 018180b4-139d-5bff-8d94-73a9f51faaad
DOI: 10.1101/2024.08.07.607012

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Latent generative modeling of long genetic sequences with GANsDOI 10.1101/2024.08.07.607012Read

Creating artificial human genomes using generative neural networks.Semantic proximity Read

Creating Artificial Human Genomes Using Generative ModelsSemantic proximity Read

GenProtect-V: A Variational Inference-based Framework for Privacy-Preserving Synthetic Human Genomic Data GenerationSemantic proximity Read

Generating realistic artificial Human genomes using adversarial autoencodersSemantic proximity Read

This population does not exist: learning the distribution of evolutionary histories with generative adversarial networksSemantic proximity Read

Deep Generative Models for Discrete Genotype SimulationSemantic proximity Read

Learning inherent genetic patterns and trait associations with deep generative models for discrete genotype simulation.Semantic proximity Read

<i>In silico</i> generation of synthetic cancer genomes using generative AISemantic proximity Read

Automatic inference of demographic parameters using Generative Adversarial NetworksSemantic proximity Read

Generative Moment Matching Networks for Genotype Simulation.Semantic proximity Read

Synthetic observations from deep generative models and binary omics data with limited sample size.Semantic proximity Read

Hidden Knowledge Recovery from GAN-generated Single-cell RNA-seq DataSemantic proximity Read