Author

Eric O. Postma

Tilburg UniversityORCID 0000-0001-9627-1523Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:dimensionality reduction· 2 Research Focus:gene regulatory network inference· 2 Assay:whole-genome sequencing· 2 Research Focus:differential expression· 1 Research Focus:GWAS / association mapping· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Alexander SchönhuthiD· 2 shared Gizem TaşiD· 2 shared Marleen BalvertiD· 2 shared A. Singleton· 1 shared Adriano ChiòiD· 1 shared Ahmad Al KhleifatiD· 1 shared Albert Christian LudolphiD· 1 shared Albert Hofman DiD· 1 shared

Activity by Year

2024-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersGenomics

Alexander SchönhuthORCID

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2 papers

Recent·Most cited

GenomicsbioRxivPreprintDec 6, 2024

PhenoEncoder: A Discriminative Embedding Approach to Genomic Data Compression

Gizem Taş, Eric O. Postma, Marleen Balvert, Alexander Schönhuth

PhenoEncoder improves genomic data compression by integrating phenotype information, enhancing downstream classification performance in complex trait studies.

DOI 10.1101/2024.12.06.625879

whole-genome sequencingdeep learning

𝕏 PostBluesky

GenomicsBioinformaticsPeer-reviewedMay 17, 2024

Computing Linkage Disequilibrium Aware Genome Embeddings using Autoencoders

Gizem Taş, Timo Westerdijk, Eric O. Postma…·Marleen Balvert

The paper introduces a method to compress genetic data using autoencoders that leverage linkage disequilibrium structure, achieving high reconstruction accuracy while reducing data size.

3 citations

humanwhole-genome sequencing

𝕏 PostBluesky