ArticleMetabolomics : Official journal of the Metabolomic Society2026
Network propagation in bipartite metabolite-reaction graphs for metabolomic data exploration.
Article in Metabolomics : Official journal of the Metabolomic Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
introductionInterpretation of metabolomic data is frequently limited by incomplete metabolite coverage and the predefined representation of biochemical organization provided by pathway-based approaches.
objectivesThis study describes and evaluates a graph-based network propagation method designed to faciitate metabolomic data exploration by diffusing node-level statistical relevance scores across a metabolic network.
methodsAn undirected bipartite metabolite-reaction network was reconstructed using the KEGG database, comprising 8236 nodes (1601 metabolites and 6635 reactions). Simulated datasets with 75% sparsity mimicking two metabolic perturbations and two no-effect control groups, and real data from a previous study were used to test the strategy. Node-level statistical signal scores were distributed across the network topology using a random walk-based diffusion operator combined with a supervised clamping procedure to preserve initially observed measurements. A topological distance mask was subsequently applied to restrict propagation to nodes located within a predefined network distance from experimentally measured metabolites.
resultsNetwork propagation redistributed statistical relevance across connected subgraphs, expanding the number of metabolic features carrying topology-informed statistical scores beyond experimentally observed metabolites in simulated and real data. In both oxidative stress and mitochondrial dysfunction simulations, significant nodes displayed non-random topological organization consistent with the simulated perturbations. Propagation of real data also identified additional unmeasured metabolites that were topologically connected to experimentally observed metabolites. In multivariate analyses, network propagation expanded the feature space and showed that it could improve clustering performance.
conclusionsThe proposed bipartite metabolite-reaction network propagation provides an exploratory tool for metabolomics. By integrating topological context with statistical relevance, this approach complements established pathway analyses for hypothesis generation and candidate feature recovery in partially observed metabolic systems.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.