Author

Victor Giurcoiu

Technical University of MunichORCID 0000-0002-1190-6954Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biochemistry· 1 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:proteomics· 2 Organism:human· 1 Research Focus:protein folding & dynamics· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1 Research Focus:variant calling· 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 Chang ShaoiD· 1 shared Daniel Paul ZolgiD· 1 shared Dexiang WangiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersBiochemistry

Mathias WilhelmORCID

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2 papers

Recent·Most cited

ChemIntelligence Enables Antibody-Free, Ultra-Low-Input Profiling of Lysine Lactylation and Diverse Acyl-Proteomes

Chang Shao, Zimeng He, Qi Yuan, Victor Giurcoiu, Xiangyu He, Xu Cao, Haoran Huang, Yuebo Zhang, Yueyang Zhang, Dexiang Wang, Qihe Jiang, Zizhang Guo, Haiping Hao, Mathias Wilhelm, Hui Ye

ChemIntelligence enables antibody-free, ultra-low-input profiling of lysine lactylation and diverse acyl-proteomes using chemical derivatization and computational methods.

bioRxiv · Aug 31, 2026 · PreprintBiochemistry

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