Abstract

Biomedical databases grow by more than a thousand new publications every day. The large volume of biomedical literature that is being published at an unprecedented rate hinders the discovery of relevant knowledge from keywords of interest to gather new insights and form hypotheses. A text-mining tool, PubTator, helps to automatically annotate bioentities, such as species, chemicals, genes, and diseases, from PubMed abstracts and full-text articles. However, the manual re-organization and analysis of bioentities is a non-trivial and highly time-consuming task. ChexMix was designed to extract the unique identifiers of bioentities from query results. Herein, ChexMix was used to construct a taxonomic tree with allied species among Korean native plants and to extract the medical subject headings unique identifier of the bioentities, which co-occurred with the keywords in the same literature. ChexMix discovered the allied species related to a keyword of interest and experimentally proved its usefulness for multi-species analysis.

Details

Title
Hierarchical network analysis of co-occurring bioentities in literature
Author
Yang, Heejung 1 ; Lee, Namgil 2 ; Park Beomjun 3 ; Park, Jinyoung 4 ; Lee, Jiho 4 ; Jang, Hyeon Seok 4 ; Yoo Hojin 3 

 Kangwon National University, Department of Pharmacy, Chuncheon, Republic of Korea (GRID:grid.412010.6) (ISNI:0000 0001 0707 9039); Bionsight, Inc., Chuncheon, Republic of Korea (GRID:grid.412010.6) 
 Bionsight, Inc., Chuncheon, Republic of Korea (GRID:grid.412010.6); Kangwon National University, Department of Information Statistics, Chuncheon, Republic of Korea (GRID:grid.412010.6) (ISNI:0000 0001 0707 9039) 
 Bionsight, Inc., Chuncheon, Republic of Korea (GRID:grid.412010.6) 
 Kangwon National University, Department of Pharmacy, Chuncheon, Republic of Korea (GRID:grid.412010.6) (ISNI:0000 0001 0707 9039) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2663154740
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.