SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Rosario Ferreira, N (Autor) Outra; Guimaraes, V (Autor) Outra; Costa, VS (Autor) FCUP; Moreira, IS (Autor) Outra

Revista

Título: BMC Bioinformatics

Vol. 22

Página Final: 482

ISSN: 1471-2105

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 7 Citações

Outras Informações

ID Authenticus: P-00V-FRB

DOI: 10.1186/s12859-021-04397-w

Abstract (EN): Background 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12