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This study extends the iDMET framework to include metabolite sets derived from differential metabolomic profiles, forming iDMET+. iDMET+ increases dataset diversity and improves pathway and metabolite-set discovery. Case studies across three cancers validate consistent enrichment when differential profiles are available and reveal coverage limitations when they are absent.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/an-enrichment-based-approach-to-interpreting-metabolomic-data-using-differential-metabolomic-profiles-within-the-idmet-framework/349304/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/an-enrichment-based-approach-to-interpreting-metabolomic-data-using-differential-metabolomic-profiles-within-the-idmet-framework/349304.png","ImageObject",300,407,{"name":92,"@type":93},"McGucket","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-25","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What limitation does the study address in conventional pathway enrichment methods for metabolomics?","Question",{"text":112,"@type":113},"Conventional approaches rely on a limited set of predefined metabolic pathways, which lowers the chance of discovering pathways linked to a specific metabolomic profile.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does iDMET+ improve upon the original iDMET framework?",{"text":117,"@type":113},"iDMET+ expands metabolite sets by incorporating sets derived from differential metabolomic profiles, increasing dataset diversity and improving the likelihood of discovering associated metabolite sets.",{"name":119,"@type":110,"acceptedAnswer":120},"What do the disease case studies show about iDMET+ performance?",{"text":121,"@type":113},"For clear cell renal cell carcinoma and colorectal cancer, iDMET+ finds enriched relevant studies when differential metabolomic profiles are available. 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BMC Bioinformatics (2026) 27:131 [https://doi.org/10.1186/s12859-026-06456-6](https://doi.org/10.1186/s12859-026-06456-6)  \nBMC Bioinformatics  \nRESEARCH Open Access  \nAn enrichment-based approach to interpreting  metabolomic data using differential metabolomic profiles within the iDMET framework  \nRira Matsuta1,2,3, Hiroyuki Yamamoto3*, Atsushi Fukushima4,5, Sho Tabata6,7,8, Hideki Makinoshima6,7,8, Tomoyoshi Soga1,2,9, Rintaro Saito1,2 and Eisuke Hayakawa10,11*  \n*Correspondence: Hiroyuki Yamamoto [h.yama2396@gmail.com](h.yama2396@gmail.com)[ ](h.yama2396@gmail.com)Eisuke Hayakawa  \n[eisuke.hayakawa@bio.kyutech.ac.jp](eisuke.hayakawa@bio.kyutech.ac.jp)[ ](eisuke.hayakawa@bio.kyutech.ac.jp)Full list of author information is available at the end of the article  \nAbstract  \nBackground Pathway enrichment analysis is a crucial method for the biological interpretation of metabolomic data by identifying associations between altered metabolites and biological pathways. However, such traditional approaches often rely on a limited set of predefined metabolic pathways, resulting in a low likelihood of discovering pathways associated with a given metabolic profile. To overcome this limitation, we extended our previously developed iDMET methodology to incorporate a broader range of metabolite sets, including those derived from differential metabolomic profiles. This enhanced approach, termed iDMET+, significantly expands dataset diversity and size, increasing the likelihood of discovering associated metabolite sets for a given metabolic profile, thereby enables more biological insights to be obtained from the metabolic profile.  \nResults We validated iDMET+ through case studies on three diseases: clear cell renal cell carcinoma, colorectal cancer, and small cell lung cancer. First, using a clear cell renal cell carcinoma study as input, iDMET+ correctly identified another study of the same disease that involved metabolomic analysis. This pair of studies was identified as relevant in our previous iDMET results, showing the consistency between iDMET+ and iDMET. Second, using the metabolomic profile of colorectal cancer as input, iDMET+ identified not only another metabolomic study of the same cancer but, surprisingly, also metabolomic studies on prostate cancer and a high-fat diet. These studies focused on MYC-driven metabolic reprogramming, which was also a major focus of the input study. In both case studies, related studies were enriched because the differential metabolomic profiles of directly associated studies were part of themetabolite set. In contrast, the small cell lung cancer study highlighted limitations in dataset coverage—the absence of directly relevant differential metabolomic profiles resulted in fewer enriched metabolite sets. Nevertheless, the analysis of commonly altered metabolites still yielded some meaningful results. Metabolite alterations associated with inhibition of the purine salvage pathway were observed, suggesting potential involvement in tumor metabolic reprogramming.  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/.)[.](http://creativecomm","cbCaii260UZNzw5r","https://ap.wps.com/l/cbCaii260UZNzw5r","pdf",1682114,16,"English","# Abstract\n## Background\n## Results\n## Conclusions\n# Keywords\n# Background\n## Pathway enrichment analysis in metabolomics\n## Related methods and tools","[{\"question\":\"What limitation does the study address in conventional pathway enrichment methods for metabolomics?\",\"answer\":\"Conventional approaches rely on a limited set of predefined metabolic pathways, which lowers the chance of discovering pathways linked to a specific metabolomic profile.\"},{\"question\":\"How does iDMET+ improve upon the original iDMET framework?\",\"answer\":\"iDMET+ expands metabolite sets by incorporating sets derived from differential metabolomic profiles, increasing dataset diversity and improving the likelihood of discovering associated metabolite sets.\"},{\"question\":\"What do the disease case studies show about iDMET+ performance?\",\"answer\":\"For clear cell renal cell carcinoma and colorectal cancer, iDMET+ finds enriched relevant studies when differential metabolomic profiles are available. For small cell lung cancer, results highlight reduced enrichment due to missing directly relevant differential profiles, though meaningful metabolite alterations still emerge.\"}]","An enrichment-based approach to interpreting metabolomic data using differential metabolomic profiles within the iDMET framework | PDF",1790082679]