Author

Sonja Katz

Erasmus MCORCID 0000-0002-9958-0724Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Assay:DNA methylation· 1 Assay:multiome· 1 Approach:variational autoencoder· 1

Frequent Co-Authors

Edoardo SaccentiiD· 2 shared Gennady V. RoshchupkiniD· 2 shared Vítor A. P. Martins dos SantosiD· 2 shared David W. FardoiD· 1 shared Kristel Van SteeniD· 1 shared Peter D. ClaesiD· 1 shared Zuqi LiiD· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 21 mutual co-author links

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2 shared papersGenomics

Edoardo SaccentiORCID

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2 papers

Recent·Most cited

Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping

Zuqi Li, Sonja Katz, Edoardo Saccenti, David W. Fardo, Peter D. Claes, Vítor A. P. Martins dos Santos, Kristel Van Steen, Gennady V. Roshchupkin

The paper introduces four VAE-based deconfounding frameworks for multi-omics clustering, showing they reduce confounding effects and improve biological clustering.

Briefings in Bioinformatics · Sep 23, 2024Peer-reviewedGenomics

mEthAE: an Explainable AutoEncoder for methylation data

Sonja Katz, Vítor A. P. Martins dos Santos, Edoardo Saccenti, Gennady V. Roshchupkin

The paper introduces mEthAE, an autoencoder for methylation data that reduces dimensions 400-fold while maintaining accuracy and identifies biologically significant CpG sites through perturbation-based interpretation.

bioRxiv · Jul 18, 2023 · PreprintBioinformatics