Author

Robin Durnik

Masaryk UniversityORCID 0000-0002-4301-6761Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Research Focus:database / resource· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:human· 1 Approach:large language model· 1 Assay:metagenomics· 1 Assay:proteomics· 1

Frequent Co-Authors

Helge HechtiD· 2 shared Lola Murielle BajardiD· 2 shared Ludek BlahaiD· 2 shared Florence JornodiD· 1 shared Iva SovadinovaiD· 1 shared Joost B. BeltmaniD· 1 shared Karine AudouzeiD· 1 shared Levi M. T. WinkelmaniD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersBioinformatics

Helge HechtORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintNov 9, 2026

AOPTK: Open-Source Software for AI-Assisted Toxicological Literature Analysis

Robin Durnik, Lola Murielle Bajard, Ludek Blaha…·Helge Hecht

AOPTK automates toxicological literature analysis using AI, achieving high accuracy in identifying chemical-biological effect relationships.

DOI 10.64898/2026.10.29.761316

humanproteomics

𝕏 PostBluesky

Pharmacology & ToxicologyToxicological SciencesPeer-reviewedJul 29, 2026

Towards Bayesian-based quantitative adverse outcome pathways using in vitro data from open literature and continuous variables: a case example for liver fibrosis.

Robin Durník, Tereza Juchelková, Helge Hecht…·Lola Bajard

This paper develops a Bayesian-based model using in vitro data and continuous variables to predict liver fibrosis by analyzing key markers of hepatic stellate cell activation.

DOI 10.1093/toxsci/kfag090

Bayesian inferencebenchmarking

𝕏 PostBluesky