DISEASES: Text mining and data integration of disease–gene associations

By Sune Frankild, Albert Pallejà, Kalliopi Tsafou, Janos X. Binder, Lars Juhl Jensen. bioRxiv, 2014

567 citations recorded by the source corpus.

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease–gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease–gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a user-friendly web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/94c48a1fe7ef343481c3a82a9476e3d3b910948a

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Citations in the corpus

The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible (2016, background): “The second option is to retrieve a network for a disease of interest; it first re-trieves a list of the top-N human proteins associated with the disease from the DISEASES database (46) and subsequently loads the STRING network for these proteins into Cytoscape.”
Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings (2019, methodology): “In SPOKE, the ASSOCIATES_- DaG edges represent known associations between Diseases and Genes and were obtained from the GWAS Catalog 15 , DISEASES 16 , DisGeNET 17,18 , and DOAF 19 .”
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources (2020, background): “Other work focuses on the task of associating genes with diseases [267] or finding associations between metabolites, proteins, genes, and diseases [258].”