{
  "abstract": "Background: The four molecular subtypes of endometrial carcinoma defined by The Cancer Genome Atlas (TCGA) can be reconstructed from multi-omics data, but these data contain about 460,000 features for 369 patients, the subtypes are imbalanced, and feature selection easily leaks test-set information. Methods: We developed POISE-X, a leakage-controlled, imbalance-aware benchmarking pipeline with an interchangeable metaheuristic search engine. Using RNA-seq, miRNA, DNA methylation, copy-number, mutation, and clinical data from 369 TCGA-UCEC patients, we compared 19 optimizers under nested five-fold cross-validation, with in-fold prefiltering to 2505 candidate features and a macro-averaged F1 fitness function. Results: Performance differed across optimizers in a Friedman test on fold means (p=1.989×10−6). No selected panel exceeded logistic regression on the full candidate pool, which reached a macro-averaged F1 of 0.902; the compact configuration chosen by our selection rule, POISE-GWO, retained 443 features with a macro-averaged F1 of 0.880, and the selection rule itself scored 0.881 under leave-one-fold-out evaluation, and an ANOVA-ranked panel of 450 features reached 0.886. These results held without clinical variables and, in the two searches tested, for parsimony weights of 0.8–0.99. A surrogate of the clinical molecular classification built from POLE exonuclease-domain mutation, mismatch-repair deficiency, and TP53 mutation reached a macro-averaged F1 of 0.924, higher on average than every multi-omics model. Conclusions: In this cohort, wrapper search therefore compressed the feature set without adding discriminative power, and the selected panels reconstructed an existing classification, not a new treatment-relevant signature. POISE-X is a framework for benchmarking feature-selection strategies; any selected panel requires external validation before clinical use.",
  "abstract_shown": true,
  "affiliations": [
    "Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Minyue Cao"
    },
    {
      "affs": [
        0
      ],
      "name": "Yiqin Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yan Ding"
    },
    {
      "affs": [
        0
      ],
      "name": "Genyi Jiang",
      "orcid": "0000-0003-1548-5533"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiayu Yan"
    },
    {
      "affs": [
        0
      ],
      "name": "Yihan Sun"
    },
    {
      "affs": [
        0
      ],
      "name": "Yanli Zhang",
      "orcid": "0000-0002-8265-3499"
    },
    {
      "affs": [
        0
      ],
      "name": "Luyao Kang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jing Luo"
    },
    {
      "affs": [
        0
      ],
      "name": "Bilan Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3z7jket41q55x.bib",
  "cited_by": 0,
  "doi": "10.3390/cancers18203353",
  "fulltext_url": null,
  "id": "3z7jket41q55x",
  "issns": [
    "2072-6694"
  ],
  "landing_url": "https://www.mdpi.com/2072-6694/18/20/3353",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Gene expression and cancer classification"
    ]
  ],
  "openalex": "W7222762953",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/3z7jket41q55x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:58Z",
      "native_id": "10.3390/cancers18203353",
      "source": "crossref",
      "url": "https://www.mdpi.com/2072-6694/18/20/3353"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Gene expression and cancer classification",
      "subfield": "molecular-biology"
    }
  ],
  "title": "A Leakage-Controlled Benchmark of Metaheuristic Multi-Omics Feature Selection for Reconstructing TCGA Molecular Subtypes in Endometrial Cancer",
  "title_text": "A Leakage-Controlled Benchmark of Metaheuristic Multi-Omics Feature Selection for Reconstructing TCGA Molecular Subtypes in Endometrial Cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:58Z",
  "url": "https://research.draconitic.com/paper/3z7jket41q55x",
  "venue": "Cancers",
  "versions": []
}