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Open AccessArticle

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

1
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, China
2
Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming 650050, China
3
College of Engineering and Design, Hunan Normal University, Changsh, 410081, China
*
Author to whom correspondence should be addressed.
Genes 2020, 11(2), 153; https://doi.org/10.3390/genes11020153
Received: 17 December 2019 / Revised: 27 January 2020 / Accepted: 29 January 2020 / Published: 31 January 2020
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.
Keywords: human essential genes; protein–protein interaction network; network embedding; feature representation human essential genes; protein–protein interaction network; network embedding; feature representation
MDPI and ACS Style

Dai, W.; Chang, Q.; Peng, W.; Zhong, J.; Li, Y. Network Embedding the Protein–Protein Interaction Network for Human Essential Genes Identification. Genes 2020, 11, 153.

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