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Review

The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining

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CQC-Coimbra Chemistry Center, Chemistry Department, Faculty of Science and Technology, University of Coimbra, 3004-535 Coimbra, Portugal
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CIBB, University of Coimbra, 3000-456 Coimbra, Portugal
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IIIs-Institute for Interdisciplinary Research, University of Coimbra, 3000-456 Coimbra, Portugal
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Department of Sciences, University of Porto, 4169-007 Porto, Portugal
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INESC-TEC-Centre of Advanced Computing Systems, 4169-007 Porto, Portugal
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Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
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CNC-Center for Neuroscience and Cell Biology, CIBB-Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-535 Coimbra, Portugal
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Authors to whom correspondence should be addressed.
Academic Editor: Yehia Mechref
BioChem 2021, 1(2), 60-80; https://doi.org/10.3390/biochem1020007
Received: 12 June 2021 / Revised: 12 July 2021 / Accepted: 14 July 2021 / Published: 27 July 2021
(This article belongs to the Special Issue Computational Analysis of Proteomes and Genomes)
Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category. View Full-Text
Keywords: text mining; biomedical articles; artificial intelligence; deep learning; machine learning text mining; biomedical articles; artificial intelligence; deep learning; machine learning
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MDPI and ACS Style

Rosário-Ferreira, N.; Marques-Pereira, C.; Pires, M.; Ramalhão, D.; Pereira, N.; Guimarães, V.; Santos Costa, V.; Moreira, I.S. The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining. BioChem 2021, 1, 60-80. https://doi.org/10.3390/biochem1020007

AMA Style

Rosário-Ferreira N, Marques-Pereira C, Pires M, Ramalhão D, Pereira N, Guimarães V, Santos Costa V, Moreira IS. The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining. BioChem. 2021; 1(2):60-80. https://doi.org/10.3390/biochem1020007

Chicago/Turabian Style

Rosário-Ferreira, Nícia, Catarina Marques-Pereira, Manuel Pires, Daniel Ramalhão, Nádia Pereira, Victor Guimarães, Vítor Santos Costa, and Irina S. Moreira 2021. "The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining" BioChem 1, no. 2: 60-80. https://doi.org/10.3390/biochem1020007

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