A Novel Computational Method for Predicting LncRNA-Disease Associations from Heterogeneous Information Network with SDNE Embedding Model

Zhang, Ping; Zhao, Bo-Wei; Wong, Leon; You, Zhu‐Hong; Guo, Zhen-Hao; Yi, Hai-Cheng

doi:10.1007/978-3-030-60802-6_44

Cited by 6 publications

(2 citation statements)

References 55 publications

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“…Specifically, the authors first constructed five networks, including the drug–substructure network, drug–target network, drug–enzyme network, drug–pathway network and DDI network. For drug d i , the authors applied structural deep network embedding method [ 140 , 141 ] to learn its embeddings (i.e. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${E}_i^s$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${E}_i^t$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${E}_i^e$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${E}_i^p$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${E}_i^d$\end{document} ) from the above networks, respectively.…”

Section: Drugmentioning

confidence: 99%

Drug–drug interaction prediction: databases, web servers and computational models

Zhao,

Yin,

Zhang

et al. 2023

Briefings in Bioinformatics

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In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug–drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.

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Section: Drugmentioning

confidence: 99%

Drug–drug interaction prediction: databases, web servers and computational models

Zhao,

Yin,

Zhang

et al. 2023

Briefings in Bioinformatics

View full text Add to dashboard Cite

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“…A predictive lncRNA-disease prediction model based on heterogeneous networks is proposed by Song et al (2020). The LDAMAN is proposed by Zhang et al (2020), which uses a structural deep network embedding model. The method based on linear neighborhood similarity and unbalanced double random walk (LDA-LNSUBRW) is proposed by Xie et al (2020a).…”

Section: Introductionmentioning

confidence: 99%

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

Duan

Kuang

Wang

et al. 2021

Front. Cell Dev. Biol.

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In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA–disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.

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A Multi-graph Deep Learning Model for Predicting Drug-Disease Associations

Zhao

You

et al. 2021

Intelligent Computing Theories and Application

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A Novel Computational Method for Predicting LncRNA-Disease Associations from Heterogeneous Information Network with SDNE Embedding Model

Cited by 6 publications

References 55 publications

Drug–drug interaction prediction: databases, web servers and computational models

Drug–drug interaction prediction: databases, web servers and computational models

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

A Multi-graph Deep Learning Model for Predicting Drug-Disease Associations

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