Modern microbiological research generates vast quantities of data, particularly from high-throughput "omics" technologies. These datasets promise unprecedented insights into microbial diversity, function, and interactions, yet they are often deposited in unstructured formats with inadequate metadata, significantly hamp...
K. Förstner, Sina-Victoria Barysch, Anke Becker et al.· Research Ideas and Outcomes· 0 citations
Abstract Maintaining scientific databases that depend on continuous curation of research literature often requires labor-intensive, slow, and error-prone annotation processes. To address these challenges, we present a pipeline that integrates text mining with expert supervision to support database expansion. Using the...
E. Quadros, L. Reimer, Julia Koblitz· Journal of Integrative Bioin...· 1 citation
The vast amount of existing data on microbial strains holds immense potential to revolutionize bioindustry through the application of Artificial Intelligence (AI). However, the training of robust predictive AI models requires large-scale, unified, and non-redundant microbial datasets, which is currently severely hinder...
Julius F. Witte, A. Lissin, Isabel Schober et al.· bioRxiv· 0 citations
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