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

ABSTRACT:

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 si

SUBMITTER: Rosario-Ferreira N 

PROVIDER: S-EPMC8491382 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature