{
  "abstract": "Cancer subtype identification from multi-omics profiles is important for understanding tumor heterogeneity and advancing computational precision oncology. Various subtyping methods have been proposed, among which graph-based approaches have become a widely adopted paradigm by capturing patient-to-patient relationships from heterogeneous molecular profiles. However, the reliability of learned patient similarity graphs remains a critical challenge, as noisy or spurious edges induced by high-dimensional and heterogeneous omics measurements can impair subtype discrimination. To address this issue, we propose topology-aware tensor graph learning (TaTGL), an unsupervised multi-omics clustering framework for cancer subtyping. TaTGL first constructs omics-specific patient graphs using a topology-aware distance metric that integrates molecular-profile distance with shared-neighborhood consistency. Furthermore, to exploit both complementary and consistent information across different omics views, the resulting graphs are integrated through frequency-weighted t-SVD-based tensor graph learning, which captures high-order cross-omics structural consistency. Experimental studies on several cancer subtype datasets show that TaTGL achieves competitive or superior clustering performance on labeled datasets and identifies clinically relevant patient subgroups on survival-based datasets. These results demonstrate that the proposed method can discover molecularly coherent and clinically meaningful cancer subtypes from heterogeneous multi-omics profiles.",
  "abstract_shown": true,
  "affiliations": [
    "School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA;",
    "School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hanrui Shi"
    },
    {
      "affs": [
        1
      ],
      "name": "Man-Fai Leung"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1mjy771csr1m8.bib",
  "cited_by": 0,
  "doi": "10.3390/biom16101402",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702571/?tool=EBI",
  "id": "1mjy771csr1m8",
  "issns": [
    "2218-273X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13702571",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QR1-502"
    ],
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7214368617",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702571/pdf/?tool=EBI",
  "pmcid": "PMC13702571",
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/1mjy771csr1m8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:51Z",
      "native_id": "PMC:PMC13702571",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13702571"
    },
    {
      "first_seen": "2026-10-31T14:21:22Z",
      "native_id": "002930cb25a44c5592162b05e38fd6d2",
      "source": "doaj",
      "url": "https://doi.org/10.3390/biom16101402"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Bioinformatics and Genomic Networks",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Topology-Aware Multi-Omics Tensor Graph Clustering for Cancer Subtype Identification",
  "title_text": "Topology-Aware Multi-Omics Tensor Graph Clustering for Cancer Subtype Identification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:21:22Z",
  "url": "https://research.draconitic.com/paper/1mjy771csr1m8",
  "venue": "Biomolecules",
  "versions": []
}