Dual graph convolutional neural network for predicting chemical networks

Harada, Shonosuke; Akita, Hirotaka; Tsubaki, Masashi; Baba, Yukino; Takigawa, Ichigaku; Yamanishi, Yoshihiro; Kashima, Hisashi

doi:10.1186/s12859-020-3378-0

Cited by 38 publications

(32 citation statements)

References 48 publications

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“…These graph-based methods have been successfully applied to the related tasks of chemical networks, but there are few studies that can simultaneously consider these two different types of graphs in an end-to-end manner. Harada et al (2020) used molecular graphs as nodes in chemical networks and performed internal and outer convolution operations on them. The dual graph convolutional network can capture the feature of the individual molecular graph structure and the molecular relationship network simultaneously, making excellent results in dense networks.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Graph Neural Networks and Their Current Applications in Bioinformatics

et al. 2021

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Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Graph Neural Networks and Their Current Applications in Bioinformatics

et al. 2021

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“…Much research has proved graph convolutional networks to be effective in node/graph representation learning [ 42 , 43 ]. The graph convolutional network usually extracts local substructure features for individual nodes by iteratively aggregating, transforming, and propagating information from neighbor nodes.…”

Section: Methodsmentioning

confidence: 99%

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph

Wang

Yang

et al. 2020

BMC Bioinformatics

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Background Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological network-based approaches have been proven to be effective in predicting chemical-gene interactions. Results We present CGINet, a graph convolutional network-based method for identifying chemical-gene interactions in an integrated multi-relational graph containing three types of nodes: chemicals, genes, and pathways. We investigate two different perspectives on learning node embeddings. One is to view the graph as a whole, and the other is to adopt a subgraph view that initial node embeddings are learned from the binary association subgraphs and then transferred to the multi-interaction subgraph for more focused learning of higher-level target node representations. Besides, we reconstruct the topological structures of target nodes with the latent links captured by the designed substructures. CGINet adopts an end-to-end way that the encoder and the decoder are trained jointly with known chemical-gene interactions. We aim to predict unknown but potential associations between chemicals and genes as well as their interaction types. Conclusions We study three model implementations CGINet-1/2/3 with various components and compare them with baseline approaches. As the experimental results suggest, our models exhibit competitive performances on identifying chemical-gene interactions. Besides, the subgraph perspective and the latent link both play positive roles in learning much more informative node embeddings and can lead to improved prediction.

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“…The types of DDIs can be identified by biochemical experimental (or in vivo) methods, but experimental methods are usually time-consuming, tedious and expensive and sometimes lack reproducibility (Gao et al, 2015;Fang et al, 2017). Thus, it is highly desired to develop computational methods (or in silico) for efficiently and effectively analyzing and detecting new DDI pairs, and a variety of theoretical and computational methods have been developed to predict DDI types in recent years (Herrero-Zazo et al, 2013;Cheng and Zhao, 2014;Gottlieb et al, 2014;Zhang et al, 2015;Liu et al, 2016;Takeda et al, 2017;Zhang et al, 2017a;Zhang et al, 2017b;Andrej et al, 2018;Ryu et al, 2018;Yu et al, 2018;Lee et al, 2019;Deng et al, 2020;Feng et al, 2020;Harada et al, 2020;Lin et al, 2020;Fatehifar and Karshenas, 2021;Wang et al, 2021). Computational methods can guide experimentalists designing the best experimental scheme, narrowing the scope of candidate DDIs, and provide supporting evidence for their experimental results.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, deep learning is becoming a promising technique for automatically capturing chemical compound features from data sets, and it successfully improves predictive performance. For example, Harada et al (2020) constructed a dual graph convolutional neural network to predict DDIs by combining the internal and external graph structures of drugs to learn low-dimensional representations of compounds. However, this method works well only for moderately dense chemical networks with heavy-tailed degree distributions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks

Xiao-ying¹,

Yin²,

Wu³

et al. 2021

Front. Pharmacol.

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Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug–drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining– and machine learning–based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug–drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.

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Dual graph convolutional neural network for predicting chemical networks

Cited by 38 publications

References 48 publications

Graph Neural Networks and Their Current Applications in Bioinformatics

Graph Neural Networks and Their Current Applications in Bioinformatics

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph

Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks

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