Author

Hakime Öztürk

German Cancer Research CenterORCID 0000-0002-2146-5638Unclaimed 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 Organism:human· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:GWAS / association mapping· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Oliver StegleiD· 2 shared A B Wicks· 1 shared Antti HonkelaiD· 1 shared Brian ClarkeiD· 1 shared Daniil FilienkoiD· 1 shared Eva HoltkampiD· 1 shared Felix BrechtmanniD· 1 shared Felix Munzlinger· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Oliver StegleORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMar 4, 2026

Towards Useful and Private Synthetic Omics: Community Benchmarking of Generative Models for Transcriptomics Data

Hakime Öztürk, Tejumade Afonja, Joonas Jälkö…·Antti Honkela

The paper benchmarks generative models for synthetic bulk RNA-seq data, finding trade-offs between utility, biological preservation, and privacy risks across different methods.

2 citations

humanbulk RNA-seq

BioinformaticsbioRxivPreprintJul 12, 2023

Integration of variant annotations using deep set networks boosts rare variant association genetics

Brian Clarke, Eva Holtkamp, Hakime Öztürk…·Oliver Stegle

This paper introduces DeepRVAT, a deep learning model for rare variant association genetics, showing improved gene discovery and risk prediction using whole-exome sequencing data from UK Biobank.

4 citations

humanwhole-exome sequencing