{
  "abstract": "Background. Diagnosis of infectious keratitis, uveitis, and endophthalmitis is constrained by the scarcity of pathogen-derived material in small-volume ocular specimens. The host transcriptome is an abundant alternative signal: infection elicits pathogen-class-specific transcriptional responses that may permit pathogen identification without direct detection. We tested this premise as a proof of concept using publicly available in-vitro infection RNA seq. We assembled 330 human RNA-seq samples (177 infected, 153 mock) spanning six pathogens (Herpes Simplex Virus-1, Human Cytomegalovirus Virus, Aspergillus Fumigatus, Candida Albicans, Pseudomonas Aeruginosa, Mycobacterium Tuberculosis) across 36 BioProjects and 24 host cell types. Results. Per-study differential expression yielded a robust-gene feature matrix. Pathogen classification was evaluated with Random Forest under leave-one-study-out (grouped) cross-validation and contrasted against random cross-validation to quantify study-level information leakage. Under random (non-grouped) cross-validation, balanced accuracy reached 0.85–0.92 across gene panel sizes (8–700 + genes). Under honest, leakage-corrected leave-one-group-out validation, balanced accuracy was lower than random cross-validation and remained stable across panel sizes and gene-selection strategies, peaking at 0.70 (60–100 gene panel) and averaging 0.519–0.60 across the full range of gene sets tested (8 to 9,942 genes; chance level = 0.14 for 7 classes). Unsupervised UMAP visualization of the robust gene set showed distinct clustering by pathogen and by pathogen class (viral, fungal, bacterial), with cross-kingdom pathogen pairs showing more distinct separation than within-kingdom comparisons. Conclusions. Beyond established diagnostics such as PCR and metagenomics, host transcriptomic signatures analyzed via machine learning offer a promising alternative approach to pathogen classification, achieving balanced accuracy substantially above chance under rigorous, study-independent validation. These findings support the feasibility of host-transcriptome-based diagnostic classification, with dataset provenance as the primary current limitation to clinical translation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Maria Taher Plumber¹"
    },
    {
      "name": "Ratnesh Kumar"
    },
    {
      "name": "Tejabhiram Yadavalli"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/arwajzwa40ndw.bib",
  "cited_by": 0,
  "doi": "10.21203/rs.3.rs-11233042/v1",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13734248/",
  "id": "arwajzwa40ndw",
  "issns": [
    "2693-5015"
  ],
  "landing_url": "https://doi.org/10.21203/rs.3.rs-11233042/v1",
  "language": "en",
  "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": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "2731|Ocular Infections and Treatments"
    ]
  ],
  "openalex": "W7223798819",
  "pdf_url": "https://www.researchsquare.com/article/rs-11233042/latest.pdf",
  "pmcid": "PMC13734248",
  "pmid": "42990524",
  "published": "2026-10-27",
  "publisher": "American Journal Experts",
  "ris": "https://research.draconitic.com/paper/arwajzwa40ndw.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:23:52Z",
      "native_id": "PMC13734248",
      "source": "pmc",
      "url": "https://doi.org/10.21203/rs.3.rs-11233042/v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2731|Ocular Infections and Treatments",
      "subfield": "general"
    }
  ],
  "title": "Machine Learning based Classification of Ocular Pathogens using Host-transcriptomic Signatures: a proof-of-concept",
  "title_text": "Machine Learning based Classification of Ocular Pathogens using Host-transcriptomic Signatures: a proof-of-concept",
  "type": "preprint",
  "updated_at": "2026-11-08T12:23:52Z",
  "url": "https://research.draconitic.com/paper/arwajzwa40ndw",
  "venue": "Research Square",
  "versions": []
}