{
  "abstract": "We demonstrate the framework on three infectious diseases derived from a companion mechanistic immune-simulation platform: SARS-CoV-2, Influenza A Virus, and Plasmodium falciparum. Each disease was evaluated across hospitalization and intensive care unit cohorts, yielding six cohorts in total. Best-pipeline cross-validated macro F1 ranged from 0.82 for IAV-HOSP to 0.99 for COV-ICU, and the framework produced tiered, direction-aware biomarker lists for each disease and phase. Interleukin-18 (IL-18) reached the strongest tier in both SARS-CoV-2 phases with consistent direction. When benchmarked against three separate, independently collected clinical ICU datasets, MarkerScout's top-ranked features outperformed 94.4% of randomly selected feature sets of equivalent size for SARS-CoV-2, with a weaker but directionally consistent advantage for Influenza A Virus (66.7%) and Plasmodium falciparum (60.7%).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.04268",
  "author_total": 8,
  "authors": [
    {
      "name": "Robert Moore"
    },
    {
      "name": "Frank Agayie-Ntim"
    },
    {
      "name": "Lindsey B. Crawford"
    },
    {
      "name": "M. Jana Broadhurst"
    },
    {
      "name": "David M. Brett-Major"
    },
    {
      "name": "Prakash Packrisamy"
    },
    {
      "name": "Ahmed Abdeen Hamed"
    },
    {
      "name": "Tomas Helikar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5qjhad6zkznyn.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.04268",
  "fulltext_url": null,
  "id": "5qjhad6zkznyn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.04268",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.OT"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.04268",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5qjhad6zkznyn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:19Z",
      "native_id": "2609.04268",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.04268"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.OT",
      "subfield": "general"
    }
  ],
  "title": "MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models",
  "title_text": "MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:19Z",
  "url": "https://research.draconitic.com/paper/5qjhad6zkznyn",
  "venue": "arXiv",
  "versions": []
}