Discovering latent node Information by graph attention network

Gu, Weiwei; Gao, Fei; Lou, Xiaodan; Zhang, Jiang

doi:10.1038/s41598-021-85826-x

Cited by 10 publications

(13 citation statements)

References 28 publications

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“…In node-level modelling, for all six models, the RGCN model achieved the best performance for multilabel classifcation prediction for both T and P, with accuracy rates of 0.8312 ± 0.0220 and 0.8356 ± 0.0404, respectively. Tis suggests that the graph neural network model has an advantage in node-level prediction [75]. We also performed parameter optimization experiments on the RotatE model when performing the M-T link prediction task, and the best performance was achieved when setting the epoch, embedding dimension, and learning rate to 50, 64, and 0.001, respectively.…”

Section: Discussionmentioning

confidence: 99%

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

Dan

et al. 2022

Computational Intelligence and Neuroscience

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MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

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Section: Discussionmentioning

confidence: 99%

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

Dan

et al. 2022

Computational Intelligence and Neuroscience

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“…arrival in Google Maps 25 to discovering latent node information 26 . The increasing activity in the field of GNNs has resulted into dozens of architectures being proposed in the past few years.…”

Section: Methodsmentioning

confidence: 99%

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks

Skianis¹,

Nikolentzos

Gallix

et al. 2022

Preprint

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The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. From the beginning of the outbreak, new variants have also proven to be even more contagious, accelerating the spread and stressing the healthcare system. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting positive and hospitalized cases at the department level of France via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. Our model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning.

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“…We used two classical GNNs in the basic graph neural network model, which are GraphSAGE [36] and GAT [37]. Equations 10 and 11 are the core formulations of the GraphSAGE model.…”

Section: ) Graph Neural Network Modelmentioning

confidence: 99%

“…The GAT model has a similar algorithmic process to the GraphSAGE model, requiring a process of aggregating neighbors and node updates. The difference is that weights are introduced in the process of aggregating neighbors and the information about the neighbors is weighted [37]. Equations 12, 13 and 14 are the core formulas of the GAT model.…”

mentioning

confidence: 99%

Application of a Complex Network Modeling Approach to Explore the Material Basis and Mechanisms of Traditional Chinese Medicine: A Case Study of Xuefu Zhuyu Decoction for the Treatment of Two Types of Angina Pectoris

Zhang

et al. 2022

IEEE Access

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The complex processes of action of Chinese medicines and their formulations can be studied in terms of complex heterogeneous networks, however multiple diseases are different from one disease. In this study, we proposed a graph neural network-based modelling strategy to explore the material basis and mechanisms of the formulae under multiple disease conditions, exemplified by Xuefu Zhuyu Decoction for stable angina pectoris and unstable angina pectoris. Our modelling approach consists of two parts, a feature part and a model part. In the feature part, we calculated four similarities and their combinations in the similarity network as features of nodes of the heterogeneous network, using a variety of algorithms. In the model part, both the multi-label classification model for compounds and the link prediction model for compounds-targets were performed in six model comparison experiments and four partial ablation experiments. We confirmed that the GraphSAGEbased model was the best performing multi-label classification model while the GAT-based model was the best performing link prediction model, and that the full model with various similarity contents was optimal. Good generalization ability of the model was demonstrated through generalization ability experiments. In the case study, the model was used to predict potential compound-target interactions for marker targets and the reliability of the predictions was demonstrated by molecular docking and literature mining. In conclusion, the model successfully predicted the known material basis and mechanisms and also predicted unknown compound-marker target interactions. The proposed modelling approach can be extended to the study of other formulations. INDEX TERMSHeterogeneous network; material basis; mechanism; multi-label classification; link prediction I. INTRODUCTION Chinese herbs and their constituent formulas are widely used in clinical practice to treat various types of diseases[1, 2], yet the scientific and systematic elucidation of the material basis and mechanism of action of Chinese herbs and formulas has been a challenge that This article has been accepted for publication in IEEE Access.

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Discovering latent node Information by graph attention network

Cited by 10 publications

References 28 publications

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks

Application of a Complex Network Modeling Approach to Explore the Material Basis and Mechanisms of Traditional Chinese Medicine: A Case Study of Xuefu Zhuyu Decoction for the Treatment of Two Types of Angina Pectoris

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