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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Vydané v:	IEEE transactions on emerging topics in computing Ročník 12; číslo 1; s. 163 - 176
Hlavní autori:	Zhao, Bo-Wei, Wang, Lei, Hu, Peng-Wei, Wong, Leon, Su, Xiao-Rui, Wang, Bao-Quan, You, Zhu-Hong, Hu, Lun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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Abstract	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.
AbstractList	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.
Author	Zhao, Bo-Wei You, Zhu-Hong Su, Xiao-Rui Hu, Lun Wang, Lei Wang, Bao-Quan Wong, Leon Hu, Peng-Wei
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Snippet	Drug repositioning is a promising drug development technique to identify new indications for existing drugs. However, existing computational models only make...
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SubjectTerms	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
Title	Fusing Higher and Lower-Order Biological Information for Drug Repositioning via Graph Representation Learning
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