iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations.

Journal: BMC biology
PMID: 40346532

Abstract

BACKGROUND: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns.

Authors

  • Hang Wei
    Institute of Quality Standards & Testing Technology for Agro-products, Fujian Academy of Agricultural Sciences/ Fujian Key Laboratory of Agro-products Quality and Safety, Fuzhou, 350003, China.
  • Jialu Hou
    School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
  • Yumeng Liu
    School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
  • Alexey K Shaytan
    Department of Biology, Lomonosov Moscow State University, Moscow, 119234, Russia.
  • Bin Liu
    Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Endocrinology, Neijiang First People's Hospital, Chongqing, China.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.

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