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Integrating Text Mining into the Curation of Disease Maps

Authors :
Malte Voskamp
Liza Vinhoven
Frauke Stanke
Sylvia Hafkemeyer
Manuel Manfred Nietert
Source :
Biomolecules, Vol 12, Iss 9, p 1278 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation.

Details

Language :
English
ISSN :
12091278 and 2218273X
Volume :
12
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Biomolecules
Publication Type :
Academic Journal
Accession number :
edsdoj.462f6fa0be8546188e6e98a2d0ee525e
Document Type :
article
Full Text :
https://doi.org/10.3390/biom12091278