<p>Precision diagnosis and risk stratification of Helicobacter pylori-induced gastritis using multi-omics machine learning signatures or the synergy between complex research projects and Galaxy</p>

Paul Zierep; Georg Häcker; Rolf Backofen

doi:10.7490/f1000research.1120341.1

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Zierep P, Häcker G and Backofen R. Precision diagnosis and risk stratification of Helicobacter pylori-induced gastritis using multi-omics machine learning signatures or the synergy between complex research projects and Galaxy [version 1; not peer reviewed]. F1000Research 2025, 14:1086 (slides) (https://doi.org/10.7490/f1000research.1120341.1)

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Precision diagnosis and risk stratification of Helicobacter pylori-induced gastritis using multi-omics machine learning signatures or the synergy between complex research projects and Galaxy

Paul Zierep¹, Georg Häcker, Rolf Backofen

Published 13 Oct 2025 (https://doi.org/10.7490/f1000research.1120341.1)

Author Affiliations

¹ Albert-Ludwigs-Universitat Freiburg Technische Fakultat, Freiburg, Baden-Württemberg, Germany

Copyright © 2025 Zierep P et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 13 Oct 2025

Precision diagnosis and risk stratification of Helicobacter pylori-induced gastritis using multi-omics machine learning signatures or the synergy between complex research projects and Galaxy
[version 1; not peer reviewed]

Paul Zierep¹, Georg Häcker, Rolf Backofen

Author Affiliations

¹ Albert-Ludwigs-Universitat Freiburg Technische Fakultat, Freiburg, Baden-Württemberg, Germany

Presented at

European Galaxy Days

Competing Interests

No competing interests were disclosed

Keywords

Helicobacter pylori, gastritis, precision diagnosis, risk stratification, multi-omics, machine learning, Galaxy, FAIR workflows, amplicon sequencing, 16S, ASV, LotuS2, MGnify, contamination control, negative and positive controls, batch effects, Ampvis2, phyloseq, Shiny apps, SPUN, microbiome classification, correlation analysis, ATP6V1C1, Desulfovibrionales

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