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The understanding of the concept of reference frames is essential to discuss relative motion in one or more dimensions. When we say that an object has a certain velocity, we must state the velocity with respect to a given reference frame. In most examples, this reference frame has been Earth. For instance, if a statement reads that a person is sitting in a train moving at 10 m/s east, then it implies that the person on the train is moving relative to the surface of Earth at this velocity,...
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Curatable Named-Entity Recognition Using Semantic Relations.

Yi-Yu Hsu, Hung-Yu Kao

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 11, 2015
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    Summary
    This summary is machine-generated.

    The CoINNER tool enhances biomedical database development by accurately identifying genes, chemicals, and diseases. This named-entity recognition (NER) system improves biocuration workflows and accelerates scientific discovery.

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    Area of Science:

    • Biomedical Informatics
    • Computational Biology
    • Bioinformatics

    Background:

    • Named-entity recognition (NER) is crucial for biomedical databases, but existing tools generate diverse entities, complicating curation.
    • Classifying curatable named-entities streamlines biocuration and accelerates workflows.

    Purpose of the Study:

    • To develop Co-occurrence Interaction Nexus with Named-entity Recognition (CoINNER), a web-based tool for identifying genes, chemicals, diseases, and action terms.
    • To improve the accuracy and efficiency of named-entity recognition for biomedical literature analysis.

    Main Methods:

    • CoINNER utilizes advanced algorithms, including conditional random fields (CRFs) for chemical and disease mentions, and latent Dirichlet allocation (LDA) for action terms.
    • The system builds upon a prototype for gene, chemical, and disease annotation in PubMed abstracts, incorporating state-of-the-art NER tools from BioCreative III.

    Main Results:

    • CoINNER achieved a 61.5% F-measure in the BioCreative IV CTD Track, outperforming the best F-measures of 54% for gene/protein, chemical/drug, and disease NER.
    • The tool successfully identifies and classifies key biomedical entities, facilitating interaction discovery.

    Conclusions:

    • CoINNER offers a robust and efficient solution for named-entity recognition in biomedical text, significantly aiding biocuration and database development.
    • The system's improved performance demonstrates its value in accelerating the analysis of complex biological and chemical interactions.