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Text Mining for Building Biomedical Networks Using Cancer as a Case Study

LASIGE, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
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Author to whom correspondence should be addressed.
Academic Editors: Francisco Rodrigues Pinto and Javier De Las Rivas
Biomolecules 2021, 11(10), 1430; https://doi.org/10.3390/biom11101430
Received: 9 September 2021 / Revised: 24 September 2021 / Accepted: 27 September 2021 / Published: 29 September 2021
(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)
In the assembly of biological networks it is important to provide reliable interactions in an effort to have the most possible accurate representation of real-life systems. Commonly, the data used to build a network comes from diverse high-throughput essays, however most of the interaction data is available through scientific literature. This has become a challenge with the notable increase in scientific literature being published, as it is hard for human curators to track all recent discoveries without using efficient tools to help them identify these interactions in an automatic way. This can be surpassed by using text mining approaches which are capable of extracting knowledge from scientific documents. One of the most important tasks in text mining for biological network building is relation extraction, which identifies relations between the entities of interest. Many interaction databases already use text mining systems, and the development of these tools will lead to more reliable networks, as well as the possibility to personalize the networks by selecting the desired relations. This review will focus on different approaches of automatic information extraction from biomedical text that can be used to enhance existing networks or create new ones, such as deep learning state-of-the-art approaches, focusing on cancer disease as a case-study. View Full-Text
Keywords: cancer; natural language processing; network biology; text mining cancer; natural language processing; network biology; text mining
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MDPI and ACS Style

Conceição, S.I.R.; Couto, F.M. Text Mining for Building Biomedical Networks Using Cancer as a Case Study. Biomolecules 2021, 11, 1430. https://doi.org/10.3390/biom11101430

AMA Style

Conceição SIR, Couto FM. Text Mining for Building Biomedical Networks Using Cancer as a Case Study. Biomolecules. 2021; 11(10):1430. https://doi.org/10.3390/biom11101430

Chicago/Turabian Style

Conceição, Sofia I.R., and Francisco M. Couto 2021. "Text Mining for Building Biomedical Networks Using Cancer as a Case Study" Biomolecules 11, no. 10: 1430. https://doi.org/10.3390/biom11101430

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