Abstract
BackgroundNegated biomedical events are often ignored by text-mining applications; however, such events carry scientific significance. We report on the development of BioN∅T, a database of negated sentences that can be used to extract such negated events.DescriptionCurrently BioN∅T incorporates ≈32 million negated sentences, extracted from over 336 million biomedical sentences from three resources: ≈2 million full-text biomedical articles in Elsevier and the PubMed Central, as well as ≈20 million abstracts in PubMed. We evaluated BioN∅T on three important genetic disorders: autism, Alzheimer's disease and Parkinson's disease, and found that BioN∅T is able to capture negated events that may be ignored by experts.ConclusionsThe BioN∅T database can be a useful resource for biomedical researchers. BioN∅T is freely available at http://bionot.askhermes.org/. In future work, we will develop semantic web related technologies to enrich BioN∅T.
Highlights
Negated biomedical events are often ignored by text-mining applications; such events carry scientific significance
We argue that negated events provide valuable information and may help researchers formulate research hypotheses
Finding reported instances of a gene not being associated with a disease is difficult, which is why our goal in this study is to develop a text mining application that can identify such negated relations
Summary
A large amount of published literature is available in electronic format, spurring the development of several text-mining applications that can process the available literature to automatically extract information such as protein-protein interaction and gene-disease association. Using NegScope to detect scope of negation Many text-mining applications make use of sentences to extract information from literature. We searched BioN∅T for negated sentences containing a potential autism, Alzheimer’s disease or Parkinson’s disease-related gene (list of genes obtained from [16,17,18]) and the disease name. The present study did not find strong evidence of SHANK3 polymorphisms and autism or identify any described non-synonymous mutations in our cohort These might indicate that SHANK3 doesn’t represent a major susceptibility gene for autism in the autism families ascertained from Chinese Han population. All of the category 3 false positives were caused due to the same gene, MET, which is a common English word
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