ArticleBMC bioinformatics2022
iDMET: network-based approach for integrating differential analysis of cancer metabolomics.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Network-based integration of metabolomics data from large-scale repositories.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- An enrichment-based approach to interpreting metabolomic data using differential metabolomic profiles within the iDMET framework.BMC bioinformatics · 2026Article
- Identification of plasma biomarkers in lipid metabolism for accurate prediction of Alzheimer's disease.Translational psychiatry · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundComprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study.
resultsWe developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered.
conclusionsWe believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.
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