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Peer-reviewedCC BYBriefings in bioinformatics

Metapath-guided topology and domain knowledge fusion for predicting miRNA-drug-disease triplet resistance and sensitivity associations.

  • Nan Sheng1
  • Ling Gao1
  • Xiongfeng Tang2
  • Yunzhi Liu1
  • Lan Huang1
  • Yan Wang1
  1. 1Key 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.
  2. 2The Orthopaedic Medical Center, Second Hospital of Jilin University, No. 218 Ziqiang Street, Changchun 130022, China.
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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.

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Cite

BibTeXRIS
BibTeX
@article{sheng2026metapathguided,
  title = {{Metapath-guided topology and domain knowledge fusion for predicting miRNA-drug-disease triplet resistance and sensitivity associations.}},
  author = {Nan Sheng and Ling Gao and Xiongfeng Tang and Yunzhi Liu and Lan Huang and Yan Wang},
  journal = {Briefings in bioinformatics},
  year = {2026},
  month = nov,
  doi = {10.1093/bib/bbag579},
  url = {https://europepmc.org/article/MED/42998278},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Metapath-guided topology and domain knowledge fusion for predicting miRNA-drug-disease triplet resistance and sensitivity associations.
AU  - Nan Sheng
AU  - Ling Gao
AU  - Xiongfeng Tang
AU  - Yunzhi Liu
AU  - Lan Huang
AU  - Yan Wang
PY  - 2026
DA  - 2026/11/01
JO  - Briefings in bioinformatics
DO  - 10.1093/bib/bbag579
UR  - https://europepmc.org/article/MED/42998278
AB  - 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.
SN  - 1467-5463
SN  - 1477-4054
ER  -