Author

Helge Hecht

Every paper listing this name carries an ORCID.

Identified by ORCID

Helge HechtiD2 papers · Masaryk University

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

Lola Murielle BajardiD· 2 shared Ludek BlahaiD· 2 shared Robin DurnikiD· 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

Lola Murielle BajardORCID

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2 papers

Recent·Most cited

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

Robin Durnik, Lola Murielle Bajard, Ludek Blaha, Pavel Babica, Iva Sovadinova, Helge Hecht

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

bioRxiv · Nov 9, 2026 · PreprintBioinformatics

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, Levi M. T. Winkelman, Joost B. Beltman, Xavier Coumoul, Florence Jornod, Karine Audouze, Luděk Bláha, 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.

Toxicological Sciences · Jul 29, 2026Peer-reviewedPharmacology & Toxicology