Author

Patrick Scheibe

Max Planck Institute for Human Cognitive and Brain SciencesORCID 0000-0003-0361-9063Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Neuroscience· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Assay:imaging· 1 Approach:large language model· 1 Approach:statistical model· 1

Frequent Co-Authors

Jana SchoriD· 2 shared Antoine LuttiiD· 1 shared Chih LaiiD· 1 shared Gabriel ZiegleriD· 1 shared Jörg Hackermüller AiD· 1 shared Jürgen GallinatiD· 1 shared Karsten TabelowiD· 1 shared Kerrin PineiD· 1 shared

Activity by Year

2022-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersPharmacology & Toxicology

Jana SchorORCID

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3 papers

Recent·Most cited

Pharmacology & ToxicologybioRxivPreprintJul 4, 2025

AI-Driven Science Communication: Leveraging LLMs and Knowledge Graphs for Seamless Knowledge Exchange

Patrick Scheibe, Jana Schor

EcoToxFred enables non-technical users to access environmental toxicology data through natural language queries using a conversational AI agent.

DOI 10.1101/2025.07.04.663152

deep learninglarge language model

𝕏 PostBluesky

NeuroscienceNeuroImagePeer-reviewedAug 1, 2022

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity

Siawoosh Mohammadi, Tobias Streubel, Leonie Klock…·Karsten Tabelow

The paper introduces a method to estimate local errors in multi-parameter mapping (MPM) neuroimaging data, improving group-level sensitivity by accounting for noise and artefacts.

9 citations

imagingstatistical model

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BioinformaticsBriefings in BioinformaticsPeer-reviewedJul 17, 2022

AI for predicting chemical-effect associations at the chemical universe level - deepFPlearn

Jana Schor, Patrick Scheibe, Matthias Bernt…·Jörg Hackermüller A

The paper introduces deepFPlearn, a deep learning tool that predicts chemical-effect associations at the gene/pathway level using unlabeled chemical data and achieves high prediction quality.

6 citations

𝕏 PostBluesky