{
  "abstract": "Environmental exposures such as air pollution may influence human health partly through microbiome, but statistical modeling is challenged by spatial dependence across regions and ecological correlations among taxa. We propose SpaMixed, a novel spatial mixed modeling framework for microbiome count data that incorporates both sources of dependence using conditional autoregressive priors. In simulations, SpaMixed achieved high feature selection power while maintaining low false positive rates and reduced estimation error relative to existing methods. Applied to two real studies with fine particulate matter (PM2.5) exposures, SpaMixed identified biologically plausible PM2.5-associated taxa, supporting its utility for environmental microbiome research.",
  "abstract_shown": true,
  "affiliations": [
    "Division of Biostatistics, Department of Population Health, Grossman School of Medicine, New York University, 180 Madison Avenue, 4th Floor, New York, NY, 10016, USA.",
    "School of Dentistry, Department of Microbiology and Immunology, Kyungpook National University, Daegu, Korea.",
    "Division of Pulmonary, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.",
    "Hebrew University of Jerusalem, Jerusalem, Israel.",
    "Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.",
    "NYU Laura and Isaac Perlmutter Cancer Center, New York, NY, USA.",
    "Division of Biostatistics, Department of Population Health, Grossman School of Medicine, New York University, 180 Madison Avenue, 4th Floor, New York, NY, 10016, USA. Huilin.Li@nyulangone.org."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sooran Kim"
    },
    {
      "affs": [
        0
      ],
      "name": "Chan Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Soyoung Kwak"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Fares Darawshy"
    },
    {
      "affs": [
        2
      ],
      "name": "Alexander Bain"
    },
    {
      "affs": [
        2
      ],
      "name": "Leopoldo N Segal"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Jiyoung Ahn"
    },
    {
      "affs": [
        6
      ],
      "name": "Huilin Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e4fh7gs7newd8.bib",
  "cited_by": 0,
  "doi": "10.1186/s13073-026-01770-3",
  "fulltext_url": "https://europepmc.org/articles/PMC13685090",
  "id": "e4fh7gs7newd8",
  "issns": [
    "1756-994X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42907900",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QH426-470"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2002|Spatial and Panel Data Analysis"
    ]
  ],
  "openalex": "W7165019748",
  "pdf_url": "https://europepmc.org/articles/PMC13685090?pdf=render",
  "pmcid": "PMC13685090",
  "pmid": "42907900",
  "published": "2026-09-01",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/e4fh7gs7newd8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:57Z",
      "native_id": "MED:42907900",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42907900"
    },
    {
      "first_seen": "2026-10-30T13:46:14Z",
      "native_id": "d8b8dbd5e5e7414fb03b987234243599",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s13073-026-01770-3"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "journal-openalex:F13",
      "subfield": "general"
    },
    {
      "field": "biology",
      "origin": "lcc:QH426-470",
      "subfield": "genetics-genomics"
    },
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Spatial mixed models for assessing environmental exposure effects on the microbiome.",
  "title_text": "Spatial mixed models for assessing environmental exposure effects on the microbiome.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:46:14Z",
  "url": "https://research.draconitic.com/paper/e4fh7gs7newd8",
  "venue": "Genome medicine",
  "versions": []
}