{
  "abstract": "Accurately predicting drug resistance and sensitivity is critical for understanding disease treatment mechanisms and improving therapeutic strategies. MicroRNAs (miRNAs) play essential roles in regulating drug response. However, existing computational methods mainly focus on miRNA-drug pairwise associations and overlook the disease-specific context. In this study, we propose a metapath-guided topology and domain knowledge fusion framework (MTDKF) for predicting miRNA-drug-disease triplet resistance (MRDR) and sensitivity (MRDS) associations. Specifically, we construct a triplet heterogeneous graph to model the interactions among miRNAs, drugs, and diseases. A metapath topology view encoder is designed to capture regulatory patterns between nodes. Additionally, a domain knowledge view encoder is introduced to incorporate sequence, structural, and semantic features of biological entities. These two views are further integrated to learn robust and discriminative representation. We manually curate two benchmark datasets from experimentally validated associations. Extensive experimental results demonstrate that MTDKF consistently outperforms state-of-the-art approaches across multiple evaluation metrics. Furthermore, case studies on two breast cancer drugs doxorubicin and tamoxifen demonstrate its potential in discovering novel miRNAs related to drug resistance and sensitivity under disease-specific contexts.",
  "abstract_shown": true,
  "affiliations": [
    "Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun 130012, China.",
    "The Orthopaedic Medical Center, Second Hospital of Jilin University, No. 218 Ziqiang Street, Changchun 130022, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nan Sheng",
      "orcid": "0000-0002-0306-9009"
    },
    {
      "affs": [
        0
      ],
      "name": "Ling Gao"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiongfeng Tang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yunzhi Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Lan Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yan Wang",
      "orcid": "0000-0002-4751-0708"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ajd00wbd2azk.bib",
  "cited_by": 0,
  "doi": "10.1093/bib/bbag579",
  "fulltext_url": "https://europepmc.org/articles/PMC13744293",
  "id": "8ajd00wbd2azk",
  "issns": [
    "1467-5463",
    "1477-4054"
  ],
  "landing_url": "https://europepmc.org/article/MED/42998278",
  "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": [
    [
      "openalex",
      "3002|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7225452967",
  "pdf_url": "https://europepmc.org/articles/PMC13744293?pdf=render",
  "pmcid": "PMC13744293",
  "pmid": "42998278",
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8ajd00wbd2azk.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42998278",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42998278"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "Metapath-guided topology and domain knowledge fusion for predicting miRNA-drug-disease triplet resistance and sensitivity associations.",
  "title_text": "Metapath-guided topology and domain knowledge fusion for predicting miRNA-drug-disease triplet resistance and sensitivity associations.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:34Z",
  "url": "https://research.draconitic.com/paper/8ajd00wbd2azk",
  "venue": "Briefings in bioinformatics",
  "versions": []
}