TY - GEN
T1 - NSAP
T2 - 18th International Conference on Intelligent Computing, ICIC 2022
AU - Jiao, Qiqi
AU - Jiang, Yu
AU - Zhang, Yang
AU - Wang, Yadong
AU - Li, Junyi
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Exploring the association between drugs and diseases can help to accelerate the process of drug development to a certain extent. In order to investigate the association between drugs and diseases, this paper constructs a network composed of different types of nodes, and proposes a model NSAP based on neighborhood subgraph prediction. The model captures local and global information around the target node through metagraphs and contextual graphs, respectively, and can generate node representations with rich information. In addition, in metagraphs and context diagrams, the model takes advantage of graph structures to automatically generate weights for edges, which better reflects the degree of association of different neighbor nodes with the target node. At last, the attention mechanism is used to aggregate the nodal representations generated by different metapaths in the graph, so that the final representation of the nodes incorporates different semantic information. For the edge prediction, a correlation score between drug-disease node pairs is calculated by the decoder. The experimental results have confirmed that our model does have certain effect by comparing it with state of the art method. The data and code are available at: https://github.com/jqq125/NSAP.
AB - Exploring the association between drugs and diseases can help to accelerate the process of drug development to a certain extent. In order to investigate the association between drugs and diseases, this paper constructs a network composed of different types of nodes, and proposes a model NSAP based on neighborhood subgraph prediction. The model captures local and global information around the target node through metagraphs and contextual graphs, respectively, and can generate node representations with rich information. In addition, in metagraphs and context diagrams, the model takes advantage of graph structures to automatically generate weights for edges, which better reflects the degree of association of different neighbor nodes with the target node. At last, the attention mechanism is used to aggregate the nodal representations generated by different metapaths in the graph, so that the final representation of the nodes incorporates different semantic information. For the edge prediction, a correlation score between drug-disease node pairs is calculated by the decoder. The experimental results have confirmed that our model does have certain effect by comparing it with state of the art method. The data and code are available at: https://github.com/jqq125/NSAP.
KW - Attention mechanism
KW - Drug disease association prediction
KW - Heterogeneous network
KW - Link prediction
KW - Network representation method
UR - https://www.scopus.com/pages/publications/85139873412
U2 - 10.1007/978-3-031-13829-4_7
DO - 10.1007/978-3-031-13829-4_7
M3 - 会议稿件
AN - SCOPUS:85139873412
SN - 9783031138287
T3 - Lecture Notes in Computer Science
SP - 79
EP - 91
BT - Intelligent Computing - 18th International Conference, ICIC 2022, Proceedings
A2 - Huang, De-Shuang
A2 - Jo, Kang-Hyun
A2 - Jing, Junfeng
A2 - Premaratne, Prashan
A2 - Bevilacqua, Vitoantonio
A2 - Hussain, Abir
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 7 August 2022 through 11 August 2022
ER -