Author

Jan Maarten van Dijl

University Medical Center GroningenORCID 0000-0002-5688-8438Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biochemistry· 1 Microbiology· 1 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Assay:metabolomics· 2 Assay:whole-genome sequencing· 2 Approach:experimental assay· 1 Approach:genetic perturbation / CRISPR· 1 Approach:machine learning (classical)· 1 Organism:plant (other)· 1 Research Focus:protein design· 1

Frequent Co-Authors

Kristina HaslingeriD· 2 shared Rosario del Carmen Flores-VallejoiD· 2 shared Ting HeiD· 2 shared Xiao LiiD· 2 shared Ana-Maria Radu· 1 shared Andreas Meyer· 1 shared Margriet M. W. B. HendriksiD· 1 shared Martin HeldiD· 1 shared

Activity by Year

2023-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersMicrobiology

Kristina HaslingerORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

MicrobiologybioRxivPreprintOct 9, 2025

Integrating genomics and metabolomics to accelerate the discovery of anti-MRSA natural products from the endophytic fungus Neocucurbitaria sp. VM-36

Xiao Li, Rosario del Carmen Flores-Vallejo, Ting He…·Kristina Haslinger

The paper integrates genomics and metabolomics to identify anti-MRSA natural products from the endophytic fungus Neocucurbitaria sp. VM-36, discovering a phomasetin analog with potent bactericidal activity against S. aureus.

DOI 10.1101/2025.10.09.681339

whole-genome sequencingmetabolomics

𝕏 PostBluesky

BiochemistryCurrent Research in Microbial SciencesPeer-reviewedJan 1, 2025

The endophytic fungus Cosmosporella sp. VM-42 from Vinca minor is a source of bioactive compounds with potent activity against drug-resistant bacteria

Ting He, Xiao Li, Rosario del Carmen Flores-Vallejo…·Kristina Haslinger

6 citations

plant (other)metabolomics

𝕏 PostBluesky

Synthetic BiologyACS Synthetic BiologyPeer-reviewedJan 17, 2023

Signal peptide efficiency: from high-throughput data to prediction and explanation

Stefano Grasso, Valentina Dabene, Margriet M. W. B. Hendriks…·Tjeerd van Rij

The paper evaluates 12,000 signal peptides using a high-throughput assay, trains a machine learning model to predict secretion efficiency, and explains the model's features.

57 citationstop 3%

protein sequencemachine learning (classical)

𝕏 PostBluesky