{
  "abstract": "Background: MicroRNAs (miRNAs) regulate gene expression by binding to target mRNAs. Although many tools predict miRNA-target interactions, predictions show limited concordance, and benchmarks relying on curated databases may be biased by overlap with tool-development data. Methods: We benchmarked seven tools (TargetScan, miRDB, miRmap, DIANA-microT, RNA22, miRWalk, and mirDIP) across approximately 59.7 million candidate miRNA-mRNA pairs. Each pair received an evidence-graded reference score (0-3) combining validated interactions from miRTarBase and TarBase with expression-derived evidence from paired miRNA-seq and mRNA-seq data in an induced pluripotent stem cell (iPSC) cohort of 22q11.2 deletion syndrome and control lines. Performance was evaluated using Type I error, statistical power, and false discovery proportion (FDP). Robustness was assessed by increasing reference-score stringency (truth signal) and including progressively lower-confidence predictions (model signal). Optimal cutoffs maximized Youden's J statistic. Results: Rankings were largely consistent across settings. TargetScan and miRDB maintained Type I error rates below 0.05, with TargetScan showing the lowest FDP, but both had limited power. mirDIP achieved the highest power at the cost of the highest Type I error rate. At optimal cutoffs, mirDIP best balanced sensitivity and specificity (Youden's J = 0.566), followed by DIANA-microT (J = 0.481); miRWalk and RNA22 had FDPs near the random-prediction baseline. No tool was optimal across all metrics; tool and cutoff selection should reflect whether sensitivity or specificity is prioritized.\"\"\"",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.33960",
  "author_total": 5,
  "authors": [
    {
      "name": "Congliang Jin"
    },
    {
      "name": "Weibo Niu"
    },
    {
      "name": "Zhexing Wen"
    },
    {
      "name": "Zhaohui S Qin"
    },
    {
      "name": "Ximing Ran"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dw63erh5xfh9f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.33960",
  "fulltext_url": null,
  "id": "dw63erh5xfh9f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.33960",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.GN"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1306|MicroRNA in disease regulation"
    ]
  ],
  "openalex": "W7223943657",
  "pdf_url": "https://arxiv.org/pdf/2610.33960",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dw63erh5xfh9f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:44Z",
      "native_id": "2610.33960",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.33960"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Benchmarking miRNA Target Prediction Methods",
  "title_text": "Benchmarking miRNA Target Prediction Methods",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:44Z",
  "url": "https://research.draconitic.com/paper/dw63erh5xfh9f",
  "venue": "arXiv",
  "versions": []
}