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

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Rosario Ferreira, N (Author) Other; Guimaraes, V (Author) Other; Costa, VS (Author) FCUP; Moreira, IS (Author) Other

Journal

Title: BMC Bioinformatics

Vol. 22

Final page: 482

ISSN: 1471-2105

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 7 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12