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MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm

Zhen-Hao Guo, Zhu-Hong You, Hai-Cheng Yi, Kai Zheng, Yan-Bin Wang
doi: https://doi.org/10.1101/835637
Zhen-Hao Guo
1Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
2University of Chinese Academy of Sciences, Beijing 100049, China
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Zhu-Hong You
1Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
2University of Chinese Academy of Sciences, Beijing 100049, China
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  • For correspondence: zhuhongyou@ms.xjb.ac.cn
Hai-Cheng Yi
1Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
2University of Chinese Academy of Sciences, Beijing 100049, China
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Kai Zheng
3School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
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Yan-Bin Wang
4School of Cyber Science and Technology, Zhejiang University, Hangzhou 310000, Zhejiang
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Abstract

Motivation Effectively representing the MeSH headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify.

Results In this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships) which can be constructed by the rule of tree num. Then, five graph embedding algorithms including DeepWalk (DW), LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed method, we carried out the node classification and relationship prediction tasks. The experimental results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the distinguishable ability of vectors. Thus, it can act as input and continue to play a significant role in any disease-, drug-, microbe- and etc.-related computational models. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective.

Contact zhuhongyou{at}ms.xjb.ac.cn

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 14, 2019.
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MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm
Zhen-Hao Guo, Zhu-Hong You, Hai-Cheng Yi, Kai Zheng, Yan-Bin Wang
bioRxiv 835637; doi: https://doi.org/10.1101/835637
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MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm
Zhen-Hao Guo, Zhu-Hong You, Hai-Cheng Yi, Kai Zheng, Yan-Bin Wang
bioRxiv 835637; doi: https://doi.org/10.1101/835637

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