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 textmining software and all associations are also freely available for download.
Pletscher-Frankild, Sune, et al., introduced a dictionarybased tagger combined with co-occurrence scoring and released as DISEASES resource, by integrating text mining along with genome-wide association studies and cancer mutation data REF .
DISEASES: Text mining and data integration of disease–gene associations
bioRxiv
Biology