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

• Published in: Briefings in bioinformatics Vol. 22; no. 2; pp. 2085 - 2095
• Main Authors: Guo, Zhen-Hao, You, Zhu-Hong, Huang, De-Shuang, Yi, Hai-Cheng, Zheng, Kai, Chen, Zhan-Heng, Wang, Yan-Bin
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 22.03.2021; Oxford Publishing Limited (England)
• Subjects: Algorithms; Computer applications; Embedding; Finite element method; Graphical representations; Information processing; Mathematical models; Medical Subject Headings-MeSH; Performance enhancement; Performance evaluation; Prediction models; Problem Solving Protocol; MeSHHeading2vec; graph embedding; computational prediction model; MeSH relationship network
• ISSN: 1477-4054; 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbaa037

Abstract Effectively representing Medical Subject Headings (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. 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 tree num. Then, five graph embedding algorithms including DeepWalk, 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 methods, we carried out the node classification and relationship prediction tasks. The 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 representation ability of vectors. Thus, it can serve as an input and continue to play a significant role in any computational models related to disease, drug, microbe, etc. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective.