Predicting Disease-related Associations by Heterogeneous Network Embedding

被引:0
|
作者
Xiong, Yun [1 ]
Ruan, Lu [1 ]
Guo, Mengjie [1 ]
Tang, Chunlei [2 ]
Kong, Xiangnan [3 ]
Zhu, Yangyong [1 ]
Wang, Wei [4 ]
机构
[1] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Data Sci, Shanghai, Peoples R China
[2] Harvard Med Sch, Brigham & Womens Hosp, Boston, MA USA
[3] Worcester Polytech Inst, Dept Comp Sci, Worcester, MA 01609 USA
[4] Univ Calif Los Angeles, Scalable Analyt Inst ScAi, Los Angeles, CA 90024 USA
来源
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) | 2018年
基金
中国国家自然科学基金;
关键词
Heterogeneous Network; Network Embedding; Disease-related Association Prediction; HUMAN MICRORNA; SIMILARITY; INFORMATION; VALIDATION; HETESIM;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Elucidating biological mechanisms underlying complex diseases is an important goal in biomedical research. Recent advances in biological technology have enabled the generation of massive volume of data in genomics, transcriptomics, proteomics, epigenomics, metagenomics, metabolomics, nutriomics, etc., leading to the emergence of systems biology approach to investigating complex diseases. However, most of the data remain underutilized after their initial acquisition and analysis. There is a growing gap between the generation of the multifaceted data and our ability to integrate and analyze them. Inspired by the observation that many of the aforementioned data can be represented by networks, we propose a network-based model to encapsulate the rich information provided in each database and to connect across different databases. We integrate several public databases to construct a heterogeneous network in which nodes are entities such as genes, miRNAs, diseases, and edges represent known relationships between them. One fundamental challenge is how to perform meaningful analysis on such network, overcoming the intrinsic heterogeneity. We propose a network embedding method to learn a low-dimensional vector space that best preserves the known relationships between entities. Based on the learned vector representations, entities that are close to each other but currently do not have known direct connections, are likely to have an association and therefore are good candidates for future investigation. In the experiments, we construct a heterogeneous network of genes, miRNAs and diseases using data from six public databases. To evaluate the performance of the proposed method, we predict disease-gene and disease-miRNA associations. Comparison of our novel method with several state-of-the-art methods clearly demonstrates the advantage of our method, as it is the only one that takes full advantage of the rich contextual information provided by the heterogeneous network. The encouraging results suggest that our method can provide help in identifying new hypotheses to guide future research.
引用
收藏
页码:548 / 555
页数:8
相关论文
共 50 条
  • [1] PrGeFNE: Predicting disease-related genes by fast network embedding
    Xiang, Ju
    Zhang, Ning-Rui
    Zhang, Jia-Shuai
    Lv, Xiao-Yi
    Li, Min
    METHODS, 2021, 192 : 3 - 12
  • [2] Heterogeneous network embedding enabling accurate disease association predictions
    Xiong, Yun
    Guo, Mengjie
    Ruan, Lu
    Kong, Xiangnan
    Tang, Chunlei
    Zhu, Yangyong
    Wang, Wei
    BMC MEDICAL GENOMICS, 2019, 12 (Suppl 10)
  • [3] A novel graph attention adversarial network for predicting disease-related associations
    Zhang, Jinli
    Jiang, Zongli
    Hu, Xiaohua
    Song, Bo
    METHODS, 2020, 179 : 81 - 88
  • [4] Predicting gene-disease associations from the heterogeneous network using graph embedding
    Wang, Xiaochan
    Gong, Yuchong
    Yi, Jing
    Zhang, Wen
    2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2019, : 504 - 511
  • [5] NEDD: a network embedding based method for predicting drug-disease associations
    Zhou, Renyi
    Lu, Zhangli
    Luo, Huimin
    Xiang, Ju
    Zeng, Min
    Li, Min
    BMC BIOINFORMATICS, 2020, 21 (Suppl 13)
  • [6] NEDD: a network embedding based method for predicting drug-disease associations
    Renyi Zhou
    Zhangli Lu
    Huimin Luo
    Ju Xiang
    Min Zeng
    Min Li
    BMC Bioinformatics, 21
  • [7] Predicting drug-disease associations by network embedding and biomedical data integration
    Wei, Xiaomei
    Zhang, Yaliang
    Huang, Yu
    Fang, Yaping
    DATA TECHNOLOGIES AND APPLICATIONS, 2019, 53 (02) : 217 - 229
  • [8] Prioritizing candidate disease-related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network
    Zhou, Meng
    Wang, Xiaojun
    Li, Jiawei
    Hao, Dapeng
    Wang, Zhenzhen
    Shi, Hongbo
    Han, Lu
    Zhou, Hui
    Sun, Jie
    MOLECULAR BIOSYSTEMS, 2015, 11 (03) : 760 - 769
  • [9] Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs
    Xuan, Ping
    Dong, Yihua
    Guo, Yahong
    Zhang, Tiangang
    Liu, Yong
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2018, 19 (12)
  • [10] NELDA: Prediction of LncRNA-disease Associations With Network Embedding
    Li Wei-Na
    Fan Xiao-Nan
    Zhang Shao-Wu
    PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS, 2022, 49 (07) : 1369 - 1380