ArticleFrontiers in bioinformatics2026
Network-based integrative analysis of multi-level regulatory mechanisms associated with glyphosate exposure.
Article in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the molecular effects associated with glyphosate exposure remains challenging due to the fragmentation of available evidence across heterogeneous data sources. This study aimed to integrate heterogeneous molecular evidence related to glyphosate exposure through a reproducible systems biology workflow in order to prioritize human genes, regulatory networks, and biological processes associated with glyphosate. Multiple platforms, including the Comparative Toxicogenomics Database (CTD), GeneShot, and GeneCards, were queried and complemented with artificial intelligence-assisted information retrieval. Genes present in at least two independent sources were selected, and additional candidates were obtained from transcriptomic datasets using GEO2R. Gene identifiers were standardized according to the HUGO Gene Nomenclature Committee (HGNC). Functional enrichment and protein-protein interaction (PPI) network analyses were performed using STRING and Cytoscape, and hub genes were identified using the cytoHubba plugin. In addition, upstream transcription factor analysis was conducted to identify potential regulatory drivers of the gene network. The resulting consensus dataset was subsequently analyzed using protein-protein interaction networks, functional enrichment, and upstream regulatory inference. The integrative workflow prioritized a core set of 50 genes was identified, with key hub genes including
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.