Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/103783
Title: SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations
Authors: Rosário-Ferreira, Nícia 
Guimarães, Victor
Costa, Vítor S. 
Moreira, Irina S. 
Keywords: Disease-disease associations; Natural language processing; Biomedical text-mining; Deep learning; Blood cancers
Issue Date: 4-Oct-2021
Publisher: Springer Nature
Project: LA/P/0058/2020 
UIDB/04539/2020 
UIDP/04539/2020 
POCI-01-0145-FEDER-031356 
DSAIPA/DS/0118/2020 
Serial title, monograph or event: BMC Bioinformatics
Volume: 22
Issue: 1
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
URI: https://hdl.handle.net/10316/103783
ISSN: 1471-2105
DOI: 10.1186/s12859-021-04397-w
Rights: openAccess
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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