Author

Omar Osama Shouman

Technical University of MunichORCID 0000-0002-9077-3036Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Mathias WilhelmiD· 2 shared Andreas F. R. HühmeriD· 1 shared Armin SoleymaniniyaiD· 1 shared Bernard DelangheiD· 1 shared Bernhard KüsteriD· 1 shared Daniel Paul ZolgiD· 1 shared Florian P BayeriD· 1 shared Florian SeefriediD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Mathias WilhelmORCID

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2 papers

Recent·Most cited

Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM

Wassim Gabriel, Daniel Paul Zolg, Victor Giurcoiu, Omar Osama Shouman, Polina Prokofeva, Florian Seefried, Florian P Bayer, Ludwig Lautenbacher, Armin Soleymaniniya, Karsten Schnatbaum, Johannes Zerweck, Tobias Knaute, Bernard Delanghe, Andreas F. R. Hühmer, Holger Wenschuh, , Mathias Wilhelm

This paper introduces Prosit-PTM, a deep learning model for PTM discovery that improves zero-shot prediction using a synthetic dataset and chemically-informed encoding.

bioRxiv · Nov 7, 2025 · PreprintBioinformatics

To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry

Naim Abdul-Khalek, Mario Picciani, Omar Osama Shouman, Reinhard Wimmer, Michael Toft Overgaard, Mathias Wilhelm, Simon Gregersen Echers

Pfly predicts peptide detectability in mass spectrometry using a deep learning model trained on synthetic and biological data, offering customizable classification for different experimental conditions.

Journal of Proteome Research · May 9, 2025Peer-reviewedBioinformatics