{
  "abstract": "Motivation: Differential-expression and network-based prioritization capture complementary molecular evidence. However, integrating these criteria poses a trade-off between score integration and methodological transparency. Existing approaches derive weights through optimization against predictive or benchmark metrics, or use manual or equal-weight settings without explicitly encoding the rationale for relative criterion importance. Results: We introduce AHP-CDS, an auditable candidate-ranking method that makes criterion importance explicit through weights derived from the Analytic Hierarchy Process from explicit pairwise judgments, which allows the resulting rankings to be evaluated through consistency, sensitivity and contribution analyses. The resulting pairwise judgments achieved acceptable consistency ($\\text{Consistency Ratio} = 0.050$). The leave-one-criterion-out analysis showed that ranking sensitivity followed the assigned weight ordering: the removal of Fold-change producing most of the disruption (Spearman compare to full method $\\rho = 0.796$), whereas removing betweeness causes the least ($\\rho = 0.992$). Score decomposition further made individual rank changes traceable to their evidence dimension. Contact: tue1661@gmail.com or 25001662@hus.edu.vn Availability: all code is uploaded on \\url{https://github.com/NguyenMauTue/PRIDE-breast-cancer-exosome-biomarker-discovery} Supplementary: Is all posted online",
  "abstract_shown": true,
  "affiliations": [
    "VNU University of Science"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "T. M. Nguyen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b19c5g5a2pxs7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.05.14.725271",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.05.14.725271v6.full",
  "id": "b19c5g5a2pxs7",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.05.14.725271v6",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "cancer biology"
    ],
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7161636078",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.05.14.725271v6.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b19c5g5a2pxs7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:34Z",
      "native_id": "10.64898/2026.05.14.725271",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.05.14.725271v6"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "biorxiv:cancer biology",
      "subfield": "oncology"
    }
  ],
  "title": "Transparent Weighting of Heterogeneous Evidence for Auditable Candidate Prioritization",
  "title_text": "Transparent Weighting of Heterogeneous Evidence for Auditable Candidate Prioritization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:34Z",
  "url": "https://research.draconitic.com/paper/b19c5g5a2pxs7",
  "venue": "bioRxiv",
  "versions": []
}