TY - GEN
T1 - DGPDHGCN
T2 - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
AU - Zhu, Haoran
AU - Yu, Tong
AU - Ge, Ling
AU - Wang, Jianjia
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Drug repositioning is a crucial aspect of biomedical research, and predicting drug-disease associations (DDAs) is a critical step in this process. With the development of deep learning and neural network technologies, Graph Convolutional Networks (GCNs) have achieved significant performances in this research field. Although existing DDAs models have made substantial progress, there is still need for improvement in sufficiently utilizing and integrating information from multiple biological entities. In this study, we propose a Drug-Gene-Protein-Disease Heterogeneous Graph Convolutional Network (DGPDHGCN) model for drug-disease association prediction. First, we construct a heterogeneous network from multiple data sources and establish meta-paths based on the topological information of biological entities. Then, the DGPDHGCN model learns representations of drugs and diseases from similarity and association data of those entities. Finally, we define a score function to quantify the associations between drugs and diseases. Through extensive experiments, we demonstrate that DGPDHGCN outperforms baseline models in DDAs prediction tasks in terms of metrics such as AUPR, F1-score, precision, and recall. The source code and experimental datasets can be found in https://github.com/Saxon0918/DGPDHGCN
AB - Drug repositioning is a crucial aspect of biomedical research, and predicting drug-disease associations (DDAs) is a critical step in this process. With the development of deep learning and neural network technologies, Graph Convolutional Networks (GCNs) have achieved significant performances in this research field. Although existing DDAs models have made substantial progress, there is still need for improvement in sufficiently utilizing and integrating information from multiple biological entities. In this study, we propose a Drug-Gene-Protein-Disease Heterogeneous Graph Convolutional Network (DGPDHGCN) model for drug-disease association prediction. First, we construct a heterogeneous network from multiple data sources and establish meta-paths based on the topological information of biological entities. Then, the DGPDHGCN model learns representations of drugs and diseases from similarity and association data of those entities. Finally, we define a score function to quantify the associations between drugs and diseases. Through extensive experiments, we demonstrate that DGPDHGCN outperforms baseline models in DDAs prediction tasks in terms of metrics such as AUPR, F1-score, precision, and recall. The source code and experimental datasets can be found in https://github.com/Saxon0918/DGPDHGCN
KW - drug repositioning
KW - drug-disease association
KW - graph convolutional network
KW - heterogeneous network
UR - http://www.scopus.com/inward/record.url?scp=85217281923&partnerID=8YFLogxK
U2 - 10.1109/BIBM62325.2024.10822493
DO - 10.1109/BIBM62325.2024.10822493
M3 - Conference Proceeding
AN - SCOPUS:85217281923
T3 - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
SP - 1397
EP - 1402
BT - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
A2 - Cannataro, Mario
A2 - Zheng, Huiru
A2 - Gao, Lin
A2 - Cheng, Jianlin
A2 - de Miranda, Joao Luis
A2 - Zumpano, Ester
A2 - Hu, Xiaohua
A2 - Cho, Young-Rae
A2 - Park, Taesung
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 December 2024 through 6 December 2024
ER -