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Integrating Text Mining into the Curation of Disease Maps
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, sig...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496510/ https://www.ncbi.nlm.nih.gov/pubmed/36139119 http://dx.doi.org/10.3390/biom12091278 |
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author | Voskamp, Malte Vinhoven, Liza Stanke, Frauke Hafkemeyer, Sylvia Nietert, Manuel Manfred |
author_facet | Voskamp, Malte Vinhoven, Liza Stanke, Frauke Hafkemeyer, Sylvia Nietert, Manuel Manfred |
author_sort | Voskamp, Malte |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9496510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94965102022-09-23 Integrating Text Mining into the Curation of Disease Maps Voskamp, Malte Vinhoven, Liza Stanke, Frauke Hafkemeyer, Sylvia Nietert, Manuel Manfred Biomolecules Article 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. MDPI 2022-09-10 /pmc/articles/PMC9496510/ /pubmed/36139119 http://dx.doi.org/10.3390/biom12091278 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Voskamp, Malte Vinhoven, Liza Stanke, Frauke Hafkemeyer, Sylvia Nietert, Manuel Manfred Integrating Text Mining into the Curation of Disease Maps |
title | Integrating Text Mining into the Curation of Disease Maps |
title_full | Integrating Text Mining into the Curation of Disease Maps |
title_fullStr | Integrating Text Mining into the Curation of Disease Maps |
title_full_unstemmed | Integrating Text Mining into the Curation of Disease Maps |
title_short | Integrating Text Mining into the Curation of Disease Maps |
title_sort | integrating text mining into the curation of disease maps |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496510/ https://www.ncbi.nlm.nih.gov/pubmed/36139119 http://dx.doi.org/10.3390/biom12091278 |
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