Identifying Human Essential Genes by Network Embedding Protein-Protein Interaction Network

Dai, Wei; Chang, Qi; Peng, Wei; Zhong, Jiancheng; Li, Yongjiang

doi:10.1007/978-3-030-20242-2_11

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…It also outperforms the methods that represent the PPI network by other previous approaches, i.e., Deepwalk [34] and Line [35]. Part of the contents appeared in the conference paper [39]. In this work, we comprehensively discuss the advantage of bias random walk in the course of network embedding learning, but also investigate what the feature representations of the human essential genes are.…”

Section: Introductionmentioning

confidence: 99%

Network Embedding the Protein–Protein Interaction Network for Human Essential Genes Identification

Dai

Chang

Peng

et al. 2020

Genes

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Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene data makes it possible for researchers to train a machine-learning classifier by using some features of the known human essential genes and to use the classifier to predict new human essential genes. Previous studies have found that the essentiality of genes closely relates to their properties in the protein–protein interaction (PPI) network. In this work, we propose a novel supervised method to predict human essential genes by network embedding the PPI network. Our approach implements a bias random walk on the network to get the node network context. Then, the node pairs are input into an artificial neural network to learn their representation vectors that maximally preserves network structure and the properties of the nodes in the network. Finally, the features are put into an SVM classifier to predict human essential genes. The prediction results on two human PPI networks show that our method achieves better performance than those that refer to either genes’ sequence information or genes’ centrality properties in the network as input features. Moreover, it also outperforms the methods that represent the PPI network by other previous approaches.

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Section: Introductionmentioning

confidence: 99%

Network Embedding the Protein–Protein Interaction Network for Human Essential Genes Identification

Dai

Chang

Peng

et al. 2020

Genes

Self Cite

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“…Network embedding is an algorithm to get the information from the network, which converts the nodes into a vector of low dimensional space while maximally preserves the network structural information (Dai et al, 2019 ). There are many network embedding algorithms.…”

Section: Methodsmentioning

confidence: 99%

Disease-gene prediction based on preserving structure network embedding

Tian²,

Ju³

2023

Front. Aging Neurosci.

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Many diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are caused by abnormalities or mutations of related genes. Many computational methods based on the network relationship between diseases and genes have been proposed to predict potential pathogenic genes. However, how to effectively mine the disease-gene relationship network to predict disease genes better is still an open problem. In this paper, a disease-gene-prediction method based on preserving structure network embedding (PSNE) is introduced. In order to predict pathogenic genes more effectively, a heterogeneous network with multiple types of bio-entities was constructed by integrating disease-gene associations, human protein network, and disease-disease associations. Furthermore, the low-dimension features of nodes extracted from the network were used to reconstruct a new disease-gene heterogeneous network. Compared with other advanced methods, the performance of PSNE has been confirmed more effective in disease-gene prediction. Finally, we applied the PSNE method to predict potential pathogenic genes for age-associated diseases such as AD and PD. We verified the effectiveness of these predicted potential genes by literature verification. Overall, this work provides an effective method for disease-gene prediction, and a series of high-confidence potential pathogenic genes of AD and PD which may be helpful for the experimental discovery of disease genes.

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Learning Structural Genetic Information via Graph Neural Embedding

Xie

Pei

et al. 2020

Bioinformatics Research and Applications

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Identifying Human Essential Genes by Network Embedding Protein-Protein Interaction Network

Cited by 3 publications

References 36 publications

Network Embedding the Protein–Protein Interaction Network for Human Essential Genes Identification

Network Embedding the Protein–Protein Interaction Network for Human Essential Genes Identification

Disease-gene prediction based on preserving structure network embedding

Learning Structural Genetic Information via Graph Neural Embedding

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