{
  "abstract": "Spatial multi-omics data provide complementary molecular views of tissue organization. However, their integration remains challenging due to data sparsity, technical noise, and modality-specific distributions. Here, we present Spatial Masked autoEncoder and Diffusion-based Model (SpaMEDM), an end-to-end graph representation learning framework that augments a dual-attention graph-integration backbone with masked reconstruction and diffusion-based latent noise-prediction regularization. In the masked track, randomly masked spot features are recovered by leveraging modality- and graph-specific neighborhood information. In parallel, Gaussian perturbations are applied to modality-specific latent representations at randomly sampled noise levels, and a spatial-graph-informed network is trained to predict the injected noise. This diffusion-based objective is used exclusively during training to encourage robustness to latent perturbations, rather than to perform iterative reverse diffusion or sampling during inference. Furthermore, cosine-based paired-spot directional alignment and adjacency reconstruction encourage compatible cross-modality representations and preservation of the selected graph structure. SpaMEDM flexibly supports purely spatial, dual feature–spatial, and modality-specific feature-pruned spatial graph configurations. Evaluations across diverse spatial multi-omics datasets demonstrate competitive spatial-domain identification performance and produce integrated embeddings that facilitate downstream exploratory analyses.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Chang Zhou",
      "orcid": "0009-0002-7184-0579"
    },
    {
      "name": "Mingxin Lu"
    },
    {
      "name": "Yilei Meng"
    },
    {
      "name": "Sangyu Hu"
    },
    {
      "name": "Yuping Zhu"
    },
    {
      "name": "Yizhuo Sun"
    },
    {
      "name": "Jianing Gu"
    },
    {
      "name": "Zhaoxuan Tian"
    },
    {
      "name": "Fei Yang",
      "orcid": "0009-0008-8784-5693"
    },
    {
      "name": "Jun Wang"
    },
    {
      "name": "Li Li",
      "orcid": "0000-0003-0524-7515"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bhgap2z6qptb3.bib",
  "cited_by": 0,
  "doi": "10.1093/bib/bbag520",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13738239/",
  "id": "bhgap2z6qptb3",
  "issns": [
    "1467-5463",
    "1477-4054"
  ],
  "landing_url": "https://doi.org/10.1093/bib/bbag520",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "AcademicSubjects/SCI01060"
    ],
    [
      "heading",
      "Problem Solving Protocol"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7225334070",
  "pdf_url": "https://academic.oup.com/bib/article-pdf/27/6/bbag520/73131947/bbag520.pdf",
  "pmcid": "PMC13738239",
  "pmid": "42986681",
  "published": "2026-11-05",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/bhgap2z6qptb3.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:23:15Z",
      "native_id": "PMC13738239",
      "source": "pmc",
      "url": "https://doi.org/10.1093/bib/bbag520"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Identifying multi-omics spatial domains via masked autoencoder and diffusion-based latent regularization with SpaMEDM",
  "title_text": "Identifying multi-omics spatial domains via masked autoencoder and diffusion-based latent regularization with SpaMEDM",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:23:15Z",
  "url": "https://research.draconitic.com/paper/bhgap2z6qptb3",
  "venue": "Briefings in Bioinformatics",
  "versions": []
}