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Predicting Herb-disease Associations Through Graph Convolutional Network
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Background:
In recent years, herbs have become very popular worldwide as a form of complementary
and alternative medicine (CAM). However, there are many types of herbs and diseases,
whose associations are impossible to be fully revealed. Identifying new therapeutic indications of herbs,
that is drug repositioning, is a critical supplement for new drug development. Considering that exploring
the associations between herbs and diseases by wet-lab techniques is time-consuming and laborious,
there is an urgent need for reliable computational methods to fill this gap.
:
In this study, we first preprocessed the herbs and their indications in the TCM-Suit database, a comprehensive,
accurate, and integrated traditional Chinese medicine database, to obtain the herb-disease association
network. We then proposed a novel model based on a graph convolution network (GCN) to infer
potential new associations between herbs and diseases.
Methods:
In our method, the effective features of herbs and diseases were extracted through multi-layer
GCN, then the layer attention mechanism was introduced to combine the features learned from multiple
GCN layers, and jump connections were added to reduce the over-smoothing phenomenon caused by
multi-layer GCN stacking. Finally, the recovered herb-disease association network was generated by the
bilinear decoder. We applied our model together with four other methods (including SCMFDD, BNNR,
LRMCMDA, and DRHGCN) to predict herb-disease associations. Compared with all other methods,
our model showed the highest area under the receiver operating characteristic curve (AUROC), the area
under the precision-recall curve (AUPRC), as well as the highest recall in the five-fold cross-validation.
Conclusion:
We further used our model to predict the candidate herbs for Alzheimer's disease and
found the compounds mediating herbs and diseases through the herb-compound-gene-disease network.
The relevant literature also confirmed our findings.
Bentham Science Publishers Ltd.
Title: Predicting Herb-disease Associations Through Graph Convolutional
Network
Description:
Background:
In recent years, herbs have become very popular worldwide as a form of complementary
and alternative medicine (CAM).
However, there are many types of herbs and diseases,
whose associations are impossible to be fully revealed.
Identifying new therapeutic indications of herbs,
that is drug repositioning, is a critical supplement for new drug development.
Considering that exploring
the associations between herbs and diseases by wet-lab techniques is time-consuming and laborious,
there is an urgent need for reliable computational methods to fill this gap.
:
In this study, we first preprocessed the herbs and their indications in the TCM-Suit database, a comprehensive,
accurate, and integrated traditional Chinese medicine database, to obtain the herb-disease association
network.
We then proposed a novel model based on a graph convolution network (GCN) to infer
potential new associations between herbs and diseases.
Methods:
In our method, the effective features of herbs and diseases were extracted through multi-layer
GCN, then the layer attention mechanism was introduced to combine the features learned from multiple
GCN layers, and jump connections were added to reduce the over-smoothing phenomenon caused by
multi-layer GCN stacking.
Finally, the recovered herb-disease association network was generated by the
bilinear decoder.
We applied our model together with four other methods (including SCMFDD, BNNR,
LRMCMDA, and DRHGCN) to predict herb-disease associations.
Compared with all other methods,
our model showed the highest area under the receiver operating characteristic curve (AUROC), the area
under the precision-recall curve (AUPRC), as well as the highest recall in the five-fold cross-validation.
Conclusion:
We further used our model to predict the candidate herbs for Alzheimer's disease and
found the compounds mediating herbs and diseases through the herb-compound-gene-disease network.
The relevant literature also confirmed our findings.
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