Author

Blaise Hanczar

Informatique, BioInformatique, Systèmes ComplexesORCID 0000-0002-5606-8296Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:deep learning· 2 Approach:diffusion model· 2 Approach:variational autoencoder· 2 Assay:bulk RNA-seq· 1 Research Focus:database / resource· 1 Research Focus:gene regulatory network inference· 1 Research Focus:genome assembly· 1

Frequent Co-Authors

Alice LacaniD· 1 shared Antoine DanchiniD· 1 shared Didier BoichardiD· 1 shared Edi PriftiiD· 1 shared Éric BarreyiD· 1 shared Eugeni BeldaiD· 1 shared Jean‐Daniel ZuckeriD· 1 shared Julien ChiquetiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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1 shared paperBioinformatics

Alice LacanORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

In Silico Generation of Gene Expression profiles usingDiffusion Models

Alice Lacan, Romain André, Michèle Sébag, Blaise Hanczar

This paper introduces diffusion models for generating synthetic gene expression data and shows they outperform existing methods in predictive performance.

BMC Bioinformatics · May 31, 2026Peer-reviewedBioinformatics

Deep Generative Models for Discrete Genotype Simulation

Sihan Xie, Thierry Tribout, Didier Boichard, Blaise Hanczar, Julien Chiquet, Éric Barrey

This study evaluates deep generative models for simulating discrete genotype data, showing their ability to capture genetic patterns and preserve genotype-phenotype associations.

bioRxiv · Aug 8, 2025 · PreprintBioinformatics

Interpretable and accurate prediction models for metagenomics data

Edi Prifti, Yann Chevaleyre, Blaise Hanczar, Eugeni Belda, Antoine Danchin, Karine Clément, Jean‐Daniel Zucker

The paper develops interpretable metagenomics prediction models based on microbial taxon abundance ratios, additions, and subtractions, achieving performance comparable to or better than traditional machine learning methods.

GigaScience · Mar 1, 2020Peer-reviewedBioinformatics