Functional Annotation of Genes Using Hierarchical Text Categorization

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TypeArticle
ConferenceBioLINK SIG: Linking Literature, Information and Knowledge for Biology, a Joint Meeting of The ISMB BioLINK Special Interest Group on Text Data Mining and The ACL Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics to be held in conjunction with the Conference on Intelligent Systems for Molecular Biology (ISMB 2005), June 24, 2005., Detroit, Michigan, USA
AbstractThis paper addresses the task of functional annotation of genes from biomedical literature. We view this task as a hierarchical text categorization problem with Gene Ontology as a class hierarchy. We present a novel global hierarchical learning approach that takes into account the semantics of a class hierarchy. This algorithm with AdaBoost as the underlying learning procedure significantly outperforms the corresponding flat” approach, i.e. the approach that does not consider any hierarchical information. In addition, we propose a novel hierarchical evaluation measure that gives credit to partially correct classification and discriminates errors by both distance and depth in a class hierarchy.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48063
NPARC number5763768
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Record identifier4681be12-118e-43be-8014-31379eb5aa98
Record created2009-03-29
Record modified2016-05-09
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