A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care

被引:2
|
作者
Zhang, Ping [1 ]
Zhang, Weihan [1 ]
Sun, Weicheng [1 ]
Li, Li [1 ]
Xu, Jinsheng [1 ]
Wang, Lei [2 ]
Wong, Leon [2 ,3 ]
机构
[1] Huazhong Agr Univ, Coll Informat, Hubei Key Lab Agr Bioinformat, Wuhan, Peoples R China
[2] Guangxi Acad Sci, Guangxi Key Lab Human Machine Interact & Intellige, Nanning, Peoples R China
[3] Tongji Univ, Inst Machine Learning & Syst Biol, Sch Elect & Informat Engn, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
lncRNA-disease associations; disease; graph representation learning; bridge heterogeneous information; SDNE; family medicine and primary care; PROTEIN-PROTEIN INTERACTIONS; LONG NONCODING RNAS; FUNCTIONAL SIMILARITY; DATABASE; CANCER;
D O I
10.3389/fgene.2023.1084482
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Identification of long non-coding RNAs (lncRNAs) associated with common diseases is crucial for patient self-diagnosis and monitoring of health conditions using artificial intelligence (AI) technology at home. LncRNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases and identifying their associations with diseases can aid in developing diagnostic biomarkers at the molecular level. Computational methods for predicting lncRNA-disease associations (LDAs) have become necessary due to the time-consuming and labor-intensive nature of wet biological experiments in hospitals, enabling patients to access LDAs through their AI terminal devices at any time. Here, we have developed a predictive tool, LDAGRL, for identifying potential LDAs using a bridge heterogeneous information network (BHnet) constructed via Structural Deep Network Embedding (SDNE). The BHnet consists of three types of molecules as bridge nodes to implicitly link the lncRNA with disease nodes and the SDNE is used to learn high-quality node representations and make LDA predictions in a unified graph space. To assess the feasibility and performance of LDAGRL, extensive experiments, including 5-fold cross-validation, comparison with state-of-the-art methods, comparison on different classifiers and comparison of different node feature combinations, were conducted, and the results showed that LDAGRL achieved satisfactory prediction performance, indicating its potential as an effective LDAs prediction tool for family medicine and primary care.
引用
收藏
页数:12
相关论文
共 16 条
  • [1] Heterogeneous graph neural network for lncRNA-disease association prediction
    Hong Shi
    Xiaomeng Zhang
    Lin Tang
    Lin Liu
    [J]. Scientific Reports, 12
  • [2] Heterogeneous graph neural network for lncRNA-disease association prediction
    Shi, Hong
    Zhang, Xiaomeng
    Tang, Lin
    Liu, Lin
    [J]. SCIENTIFIC REPORTS, 2022, 12 (01)
  • [3] Prediction of LncRNA-Disease Associations Based on Network Representation Learning
    Su, Xiaorui
    You, Zhuhong
    Yi, Haicheng
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2020, : 1805 - 1812
  • [4] DBNLDA: Deep Belief Network based representation learning for lncRNA-disease association prediction
    Madhavan, Manu
    Gopakumar, G.
    [J]. APPLIED INTELLIGENCE, 2022, 52 (05) : 5342 - 5352
  • [5] Heterogeneous graph attention network based on meta-paths for lncRNA-disease association prediction
    Zhao, Xiaosa
    Zhao, Xiaowei
    Yin, Minghao
    [J]. BRIEFINGS IN BIOINFORMATICS, 2022, 23 (01)
  • [6] DBNLDA: Deep Belief Network based representation learning for lncRNA-disease association prediction
    Manu Madhavan
    G. Gopakumar
    [J]. Applied Intelligence, 2022, 52 : 5342 - 5352
  • [7] Medicine and Disease Association Prediction via Attention-Based Medical Heterogeneous Information Network Representation Learning
    Zhang, Bin
    Yang, Dan
    Lin, Zhihuang
    [J]. IAENG International Journal of Computer Science, 2022, 49 (01) : 69 - 78
  • [8] Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks
    Zhou, Ji-Ren
    You, Zhu-Hong
    Cheng, Li
    Ji, Bo-Ya
    [J]. MOLECULAR THERAPY-NUCLEIC ACIDS, 2021, 23 : 277 - 285
  • [9] Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction
    Zhao, Xiaosa
    Wu, Jun
    Zhao, Xiaowei
    Yin, Minghao
    [J]. BRIEFINGS IN BIOINFORMATICS, 2023, 24 (01)
  • [10] gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network
    Wang, Li
    Zhong, Cheng
    [J]. BMC BIOINFORMATICS, 2022, 23 (01)