{
  "abstract": "Orchestra composes two independently built bioinformatics MCP servers -- RegNetAgents, which infers regulatory network topology, and CASCADE, which provides independent functional evidence (LINCS knockdown, DepMap essentiality, super-enhancer status, DoRothEA transcription-factor confidence) -- into one multi-agent LangGraph workflow. Its central claim is that requiring the two systems to agree on a candidate regulator yields a more trustworthy candidate than either alone. We test this on the TCGA tumor-acquired regulator tier, selecting each query's ten regulators by ARACNe mutual-information edge weight. Across BRCA/COAD focal genes and matched negative controls, requiring at least 2 of 4 evidence sources predicts known-driver status (IntOGen) among focal genes (odds ratio 3.15, Benjamini-Hochberg-adjusted p=0.016; 2.92 after deduplication) but shows no association among negative controls (p=0.70); a single source does not in BRCA/COAD. In a second panel (STAD) the pattern reproduces and is stronger (OR 6.71, adjusted p=0.0047), again with no association in controls. Corroboration adds value beyond MI edge weight in both panels (BRCA/COAD p=0.039; STAD p=0.0001), though modestly in BRCA/COAD. Deduplication, a permutation test and a reproduction of RegNetAgents' published results agree; a second, separately derived driver list (Vogelstein et al. 2013) is directionally consistent but too sparse to reach significance. All experiments invoke Orchestra's real agentic entry point.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.05496",
  "author_total": 1,
  "authors": [
    {
      "name": "Jose A. Bird"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/78k4cygmt8a4r.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.05496",
  "fulltext_url": null,
  "id": "78k4cygmt8a4r",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.05496",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.MA"
    ],
    [
      "arxiv",
      "q-bio.MN"
    ],
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7212002931",
  "pdf_url": "https://arxiv.org/pdf/2609.05496",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/78k4cygmt8a4r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:31:01Z",
      "native_id": "2609.05496",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.05496"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.MN",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Orchestra: Corroboration-Based Regulatory Candidate Discovery via Composed Bioinformatics MCP Agents",
  "title_text": "Orchestra: Corroboration-Based Regulatory Candidate Discovery via Composed Bioinformatics MCP Agents",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:50Z",
  "url": "https://research.draconitic.com/paper/78k4cygmt8a4r",
  "venue": "arXiv",
  "versions": []
}