{
  "abstract": "Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics generates thousands of features, but incomplete metabolite annotation and reliance on predefined pathways limit their biological interpretation. Here we present FeatureMAP, an LC-MS feature-centric computational framework that integrates multi-evidence metabolite annotation, network-guided refinement, metabolic module discovery, and structured large language model-based functional interpretation. Across ten public datasets, FeatureMAP improved MS1-based metabolite candidate prioritization while reducing annotation redundancy. In semi-synthetic benchmarks, FeatureMAP recovered coordinated metabolic perturbations, achieving 92% recovery of a simulated perturbation, and distinguished concurrent metabolic processes. Controlled benchmarks further evaluated functional interpretation under competing metabolite-level signals. Based on FeatureMAP, we additionally developed PhenoMAP, a phenotype-aware knowledge graph comprising 12,929 dysregulated metabolic modules from 501 human metabolomics studies spanning 326 phenotypes. Module-level comparisons revealed reproducible metabolic similarities across independent studies and biologically supported relationships among phenotypes. Application to Alzheimer's disease cerebrospinal fluid and cellular datasets identified convergent membrane-lipid alterations and implicated galactose-linked glycosphingolipid remodeling. Together, FeatureMAP and PhenoMAP enable network-grounded interpretation of incompletely annotated LC-MS metabolomics data and systematic investigation of metabolic dysregulation across biological conditions.",
  "abstract_shown": true,
  "affiliations": [
    "Nanyang Technological University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "name": "Y. Wang"
    },
    {
      "name": "F. Zhang"
    },
    {
      "name": "G. Peng"
    },
    {
      "name": "L. Qiu"
    },
    {
      "name": "L. Zeng"
    },
    {
      "name": "Y. Liu"
    },
    {
      "name": "J. Kinchagawat"
    },
    {
      "name": "X. Yang"
    },
    {
      "name": "C. Liu"
    },
    {
      "name": "C. Johnson"
    },
    {
      "name": "Q. Su"
    },
    {
      "name": "C. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "X. Shen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bd0et0weq7e8z.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.04.764165",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764165v1.full",
  "id": "bd0et0weq7e8z",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764165v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764165v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bd0et0weq7e8z.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T00:22:14Z",
      "native_id": "10.64898/2026.11.04.764165",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764165v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "FeatureMAP enables network-guided interpretation of untargeted metabolomics",
  "title_text": "FeatureMAP enables network-guided interpretation of untargeted metabolomics",
  "type": "preprint",
  "updated_at": "2026-11-07T00:20:59Z",
  "url": "https://research.draconitic.com/paper/bd0et0weq7e8z",
  "venue": "bioRxiv",
  "versions": []
}