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Rosário-Ferreira N, Guimarães V, Costa VS, et al. SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations. BMC Bioinformatics. 2021;22(1):482doi: 10.1186/s12859-021-04397-w.
Rosário-Ferreira, N., Guimarães, V., Costa, V. S., & Moreira, I. S. (2021). SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations. BMC bioinformatics, 22(1), 482. https://doi.org/10.1186/s12859-021-04397-w
Rosário-Ferreira, Nícia, et al. "SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations." BMC bioinformatics vol. 22,1 (2021): 482. doi: https://doi.org/10.1186/s12859-021-04397-w
Rosário-Ferreira N, Guimarães V, Costa VS, Moreira IS. SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations. BMC Bioinformatics. 2021 Oct 04;22(1):482. doi: 10.1186/s12859-021-04397-w. PMID: 34607568; PMCID: PMC8491382.
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BMC Bioinformatics. 2021 Oct 04;22(1):482. doi: 10.1186/s12859-021-04397-w.

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

BMC bioinformatics

Nícia Rosário-Ferreira, Victor Guimarães, Vítor S Costa, Irina S Moreira

Affiliations

  1. CQC - Coimbra Chemistry Center, Chemistry Department, Faculty of Science and Technology, University of Coimbra, 3004-535, Coimbra, Portugal. [email protected].
  2. CNC - Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal. [email protected].
  3. Department of Sciences, University of Porto, Porto, Portugal.
  4. INESC-TEC - Centre of Advanced Computing Systems, Porto, Portugal.
  5. Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456, Coimbra, Portugal. [email protected].
  6. CNC - Center for Neuroscience and Cell Biology, CIBB - Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal. [email protected].

PMID: 34607568 PMCID: PMC8491382 DOI: 10.1186/s12859-021-04397-w

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 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.

© 2021. The Author(s).

Keywords: Biomedical text-mining; Blood cancers; Deep learning; Disease-disease associations; Natural language processing

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