Combining multiple disambiguation methods for gene mention normalization
|Combining multiple disambiguation methods for gene mention normalization|
|Author(s)||Xia N., Lin H., Yang Z., Li Y.|
|Published in||Expert Systems with Applications|
|Keyword(s)||BioCreative II, Gene mention normalization, Gene symbol disambiguation, Web-based kernel (Extra: BioCreative II, Biomedical literature, Biomedical resource, Biomedical text, Biomedical text minings, Detection system, Disambiguation method, F-measure, False positive, Gene ontology, Rapid growth, Retrieved documents, Test data, Text mining, Web-based kernel, Wikipedia, Character recognition, Filtration, Information retrieval, Natural language processing systems, Ontology, Semantics, Genes)|
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Combining multiple disambiguation methods for gene mention normalization is a 2011 journal article written in English by Xia N., Lin H., Yang Z., Li Y. and published in Expert Systems with Applications.
The rapid growth of biomedical literature prompts pervasive concentrations of biomedical text mining community to explore methodology for accessing and managing this ever-increasing knowledge. One important task of text mining in biomedical literature is gene mention normalization which recognizes the biomedical entities in biomedical texts and maps each gene mention discussed in the text to unique organic database identifiers. In this work, we employ an information retrieval based method which extracts gene mention's semantic profile from PubMed abstracts for gene mention disambiguation. This disambiguation method focuses on generating a more comprehensive representation of gene mention rather than the organic clues such as gene ontology which has fewer co-occurrences with the gene mention. Furthermore, we use an existing biomedical resource as another disambiguation method. Then we extract features from gene mention detection system's outcome to build a false positive filter according to Wikipedia's retrieved documents. Our system achieved F-measure of 83.1% on BioCreative II GN test data. © 2011 Elsevier Ltd. All rights reserved.
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