Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/103783
DC Field | Value | Language |
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dc.contributor.author | Rosário-Ferreira, Nícia | - |
dc.contributor.author | Guimarães, Victor | - |
dc.contributor.author | Costa, Vítor S. | - |
dc.contributor.author | Moreira, Irina S. | - |
dc.date.accessioned | 2022-11-28T09:32:11Z | - |
dc.date.available | 2022-11-28T09:32:11Z | - |
dc.date.issued | 2021-10-04 | - |
dc.identifier.issn | 1471-2105 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/103783 | - |
dc.description.abstract | Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results: We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions: SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus | pt |
dc.language.iso | eng | pt |
dc.publisher | Springer Nature | pt |
dc.relation | LA/P/0058/2020 | pt |
dc.relation | UIDB/04539/2020 | pt |
dc.relation | UIDP/04539/2020 | pt |
dc.relation | POCI-01-0145-FEDER-031356 | pt |
dc.relation | DSAIPA/DS/0118/2020 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | Disease-disease associations | pt |
dc.subject | Natural language processing | pt |
dc.subject | Biomedical text-mining | pt |
dc.subject | Deep learning | pt |
dc.subject | Blood cancers | pt |
dc.subject.mesh | Data Mining | pt |
dc.subject.mesh | Knowledge | pt |
dc.subject.mesh | Deep Learning | pt |
dc.title | SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations | pt |
dc.type | article | - |
degois.publication.firstPage | 482 | pt |
degois.publication.issue | 1 | pt |
degois.publication.title | BMC Bioinformatics | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1186/s12859-021-04397-w | pt |
degois.publication.volume | 22 | pt |
dc.date.embargo | 2021-10-04 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.project.grantno | Center for Innovative Biomedicine and Biotechnology - Associate Laboratory | - |
crisitem.project.grantno | Center for Innovative Biomedicine and Biotechnology - CIBB | - |
crisitem.project.grantno | Center for Innovative Biomedicine and Biotechnology | - |
crisitem.author.researchunit | CQC - Coimbra Chemistry Centre | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.orcid | 0000-0002-7225-9287 | - |
crisitem.author.orcid | 0000-0002-3344-8237 | - |
crisitem.author.orcid | 0000-0003-2970-5250 | - |
Appears in Collections: | I&D CQC - Artigos em Revistas Internacionais I&D CNC - Artigos em Revistas Internacionais FCTUC Ciências da Vida - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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SicknessMiner-a-deeplearningdriven-textmining-tool-to-abridge-diseasedisease-associationsBMC-Bioinformatics.pdf | 1.56 MB | Adobe PDF | View/Open |
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