Author

Julia Koblitz

Leibniz Institute DSMZ – German Collection of Microorganisms and Cell CulturesORCID 0000-0002-7260-2129Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1 Assay:proteomics· 1

Frequent Co-Authors

Jörg OvermanniD· 2 shared L.C. ReimeriD· 2 shared Adrian VaelloiD· 1 shared Agustín García DoñateiD· 1 shared Ana M. P. MeloiD· 1 shared Andrey M. YurkoviD· 1 shared Artur LissiniD· 1 shared Aurora ZuzuarreguiiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersMicrobiology

Jörg OvermannORCID

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2 papers

Recent·Most cited

The StrainDiscoveryDatabase: an open framework for standardized microbial strain data

J. Witte, Artur Lissin, Isabel Schober, Christian Ebeling, Helko Lüken, Julia Koblitz, Andrey M. Yurkov, Boyke Bunk, José López-Coronado, Adrian Vaello, Aurora Zuzuarregui, Rosa Aznar, Agustín García Doñate, Ana M. P. Melo, G.J.M. Verkley, , L.C. Reimer

The paper introduces the StrainDiscoveryDatabase (SDD), a unified microbial strain dataset that resolves data fragmentation and synonymy to support AI-driven bioindustrial applications.

bioRxiv · Sep 10, 2026 · PreprintMicrobiology

Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets

Julia Koblitz, L.C. Reimer, Rüdiger Pukall, Jörg Overmann

The paper uses machine learning on curated bacterial datasets to predict phenotypic traits, showing that high-quality data improves prediction accuracy and generates new data points for the BacDive database.

Communications Biology · Jun 7, 2025Peer-reviewedBioinformatics