Author

Miklós Dióssy

Boston Children's HospitalORCID 0000-0003-0308-6615Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Approach:statistical model· 2 Assay:whole-exome sequencing· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Zoltán SzállásiiD· 3 shared Aurél György PrósziD· 2 shared István CsabaiiD· 2 shared Zsófia M. SztupinszkiiD· 2 shared Alexander J. NeiliD· 1 shared Anders Gorm PederseniD· 1 shared Aron C. EklundiD· 1 shared Bogumil Zimon· 1 shared

Activity by Year

2019-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersBioinformatics

Zoltán SzállásiORCID

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3 papers

Recent·Most cited

BioinformaticsJCO Precision OncologyPeer-reviewedSep 1, 2026

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

Olivera Stojkova, Judit Börcsök, Zsófia Sztupinszki…·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.

DOI 10.1200/po-26-00329

humanwhole-exome sequencing

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BioinformaticsbioRxivPreprintMay 11, 2026

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…·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.

DOI 10.64898/2026.01.28.702211

large language modelbenchmark study

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BioinformaticsbioRxivPreprintNov 28, 2019

Strand Orientation Bias Detector (SOBDetector) to remove FFPE sequencing artifacts

Miklós Dióssy, Zsófia M. Sztupinszki, Marcin Krzystanek…·Zoltán Szállási

SOBDetector removes FFPE sequencing artifacts by using a Bayesian logistic regression model trained on TCGA whole exomes, achieving high accuracy and AUC.

3 citations

humanwhole-exome sequencing

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