Fusing Higher and Lower-Order Biological Information for Drug Repositioning via Graph Representation Learning

Drug repositioning is a promising drug development technique to identify new indications for existing drugs. However, existing computational models only make use of lower-order biological information at the level of individual drugs, diseases and their associations, but few of them can take into acc...

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Veröffentlicht in:IEEE transactions on emerging topics in computing Jg. 12; H. 1; S. 163 - 176
Hauptverfasser: Zhao, Bo-Wei, Wang, Lei, Hu, Peng-Wei, Wong, Leon, Su, Xiao-Rui, Wang, Bao-Quan, You, Zhu-Hong, Hu, Lun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Alzheimer's disease
Artificial neural networks
Biological system modeling
Computational modeling
Diseases
Drug repositioning
drug-disease association
Drugs
graph representation learning
Graph representations
Graphical representations
higher and lower-order information
information fusion
Learning
Neoplasms
Predictive models
Proteins
Representation learning
ISSN:2168-6750, 2168-6750
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Zusammenfassung:Drug repositioning is a promising drug development technique to identify new indications for existing drugs. However, existing computational models only make use of lower-order biological information at the level of individual drugs, diseases and their associations, but few of them can take into account higher-order connectivity patterns presented in biological heterogeneous information networks (HINs). In this work, we propose a novel graph representation learning model, namely FuHLDR, for drug repositioning by fusing higher and lower-order biological information. Specifically, given a HIN, FuHLDR first learns the representations of drugs and diseases at a lower-order level by considering their biological attributes and drug-disease associations (DDAs) through a graph convolutional network model. Then, a meta-path-based strategy is designed to obtain their higher-order representations involving the associations among drugs, proteins and diseases. Their integrated representations are thus determined by fusing higher and lower-order representations, and finally a Random Vector Functional Link Network is employed by FuHLDR to identify novel DDAs. Experimental results on two benchmark datasets demonstrate that FuHLDR performs better than several state-of-the-art drug repositioning models. Furthermore, our case studies on Alzheimer's disease and Breast neoplasms indicate that the rich higher-order biological information gains new insight into drug repositioning with improved accuracy.
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ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2023.3239949