{
  "abstract": "Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.19569",
  "author_total": 10,
  "authors": [
    {
      "name": "Tohid Ghasemnejad"
    },
    {
      "name": "Ahmadreza Argha"
    },
    {
      "name": "Mark Grosser"
    },
    {
      "name": "John Wang"
    },
    {
      "name": "Min Yang"
    },
    {
      "name": "Thantrira Porntaveetus"
    },
    {
      "name": "Tony Roscioli"
    },
    {
      "name": "Nigel H. Lovell"
    },
    {
      "name": "Mahmoud Aarabi"
    },
    {
      "name": "Hamid Alinejad-Rokny"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3est4x5hshqr2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.19569",
  "fulltext_url": null,
  "id": "3est4x5hshqr2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.19569",
  "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",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.CL"
    ],
    [
      "arxiv",
      "q-bio.GN"
    ],
    [
      "openalex",
      "1311|Genomics and Rare Diseases"
    ]
  ],
  "openalex": "W7213592864",
  "pdf_url": "https://arxiv.org/pdf/2609.19569",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3est4x5hshqr2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:08Z",
      "native_id": "2609.19569",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.19569"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Large Language Model Agents for Evidence Based Genetic Disease Severity Classification",
  "title_text": "Large Language Model Agents for Evidence Based Genetic Disease Severity Classification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:08Z",
  "url": "https://research.draconitic.com/paper/3est4x5hshqr2",
  "venue": "arXiv",
  "versions": []
}