Author

Nils Mechtel

Science for Life LaboratoryORCID 0000-0002-1278-7125Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:imaging· 2 Research Focus:software / tool· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Assay:metabolomics· 1

Frequent Co-Authors

Wei OuyangiD· 2 shared Alice LimoncieliD· 1 shared Ananya Kedige RaoiD· 1 shared Anna KreshukiD· 1 shared Anwai ArchitiD· 1 shared Arrate Muñoz-Barrutia· 1 shared Beatriz Serrano-Solano· 1 shared C. A. SchmidtiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Wei OuyangORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintApr 22, 2026

BioEngine: scalable execution and adaptation of bioimage AI through agent-readable interfaces

Nils Mechtel, Hugo Dettner Källander, Songtao Cheng…·Wei Ouyang

BioEngine enables biologists to run, adapt, and deploy bioimage AI models on various hardware by interacting with an AI agent through a user-friendly interface.

DOI 10.64898/2026.04.19.719496

imagingexperimental assay

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BioinformaticsbioRxivPreprintDec 20, 2025

MetAlyzer to Perform Streamlined, Interactive, and Pathway-Mapping Analysis of Targeted Metabolomics Data from the biocrates Platform

Qian-Wu Liao, Luis Herfurth, C. A. Schmidt…·Gernot Poschet

MetAlyzer streamlines and visualizes targeted metabolomics data from biocrates, enabling interactive analysis without coding.

DOI 10.64898/2025.12.17.692011

metabolomicsstatistical model

𝕏 PostBluesky

Bioinformaticsnpj ImagingPeer-reviewedJul 8, 2025

FeatureForest: the power of foundation models, theusability of random forests

Mehdi Seifi, Damian E. Dalle Nogare, Juan Manuel Battagliotti…·Joran Deschamps

FeatureForest uses foundation model embeddings with random forests to enable fast, accurate segmentation of complex biological images with minimal labeling.

4 citations

imagingdeep learning

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