Author

Aurél György Prósz

Danish Cancer SocietyORCID 0000-0002-8494-6340Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:differential expression· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:large language model· 1 Approach:statistical model· 1

Frequent Co-Authors

Miklós DióssyiD· 2 shared Zoltán SzállásiiD· 2 shared Alexander J. NeiliD· 1 shared Bogumil Zimon· 1 shared Claus Storgaard SørenseniD· 1 shared István CsabaiiD· 1 shared Judit BörcsökiD· 1 shared Kent William MouwiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Miklós DióssyORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Identification of an ERCC2 mutation associated mutational signature of nucleotide excision repair deficiency in targeted panel sequencing data

Olivera Stojkova, Judit Börcsök, Zsófia Sztupinszki, Miklós Dióssy, Aurél György Prósz, Alexander J. Neil, Kent William Mouw, Claus Storgaard Sørensen, Zoltán Szállási

The paper identifies an ERCC2 mutation-associated mutational signature in targeted panel sequencing data linked to nucleotide excision repair deficiency in bladder cancer and shows its clinical relevance.

JCO Precision Oncology · Sep 1, 2026Peer-reviewedBioinformatics

Zero-shot biological reasoning with open-weights large language models reproduces CRISPR screen based prediction of synthetic lethal interactions.

Aurél György Prósz, Zsófia M. Sztupinszki, Miklós Dióssy, Oz Kilim, Bogumil Zimon, István Csabai, Zoltán Szállási

This paper uses open-weight large language models to predict synthetic lethal interactions, outperforming random chance and non-LLM methods in reproducing CRISPR screen results.

bioRxiv · May 11, 2026 · PreprintBioinformatics