Author

Aurélien Decelle

Universidad Complutense de MadridORCID 0000-0002-3017-0858Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:database / resource· 2 Approach:deep learning· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Organism:human· 1 Research Focus:imputation· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Burak YelmeniD· 2 shared Cyril FurtlehneriD· 2 shared Flora JayiD· 2 shared Antoine SzatkownikiD· 1 shared Corentin TalleciD· 1 shared Davide MarnettoiD· 1 shared Francesco MontinaroiD· 1 shared Guillaume CharpiatiD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersGenomics

Burak YelmenORCID

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2 papers

Recent·Most cited

Deep convolutional and conditional neural networks for large-scale genomic data generation

Burak Yelmen, Aurélien Decelle, Leila Lea Boulos, Antoine Szatkownik, Cyril Furtlehner, Guillaume Charpiat, Flora Jay

This paper introduces convolutional and conditional neural networks for generating large-scale artificial genomes with high SNP numbers while minimizing privacy leakage.

PLoS Computational Biology · Oct 30, 2023Peer-reviewedGenomics

Creating Artificial Human Genomes Using Generative Models

Burak Yelmen, Aurélien Decelle, Linda Ongaro, Davide Marnetto, Corentin Tallec, Francesco Montinaro, Cyril Furtlehner, Luca Pagani, Flora Jay

This study uses generative models to create artificial human genomes that can improve genetic research without compromising privacy.

PLoS Genetics · Feb 4, 2021Peer-reviewedBioinformatics