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Investigators at Aston University Report Findings in Bioinformatics (Event trigger identification for biomedical events extraction using domain...

July 8, 2014

Investigators at Aston University Report Findings in Bioinformatics (Event trigger identification for biomedical events extraction using domain knowledge)

By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators publish new report on Bioinformatics. According to news reporting from Birmingham, United Kingdom, by VerticalNews journalists, research stated, "In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases."

The news correspondents obtained a quote from the research from Aston University, "As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier."

According to the news reporters, the research concluded: "Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework."

For more information on this research see: Event trigger identification for biomedical events extraction using domain knowledge. Bioinformatics, 2014;30(11):1587-1594. Bioinformatics can be contacted at: Oxford Univ Press, Great Clarendon St, Oxford OX2 6DP, England. (Oxford University Press -; Bioinformatics -

Our news journalists report that additional information may be obtained by contacting D.Y. Zhou, Aston University, Sch Engn & Appl Sci, Birmingham B4 7ET, W Midlands, United Kingdom. Additional authors for this research include D.Y. Zhong and Y.L. He.

Keywords for this news article include: Europe, Birmingham, United Kingdom, Bioinformatics

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Source: Information Technology Newsweekly

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