Normalization of Gene/Protein Names in Biological Literatures using Vector-Space Model

As the number of biological literatures grows exponentially, needs for text mining system are increased. In text mining area, normalization is mapping gene/protein names to a database. It is necessary to combine extracted information from various literatures and to curate a database or an ontology using literatures. Previous normalization researches used direct comparison methods between a database and literatures, but it is weak to extremely variational gene/protein names in literatures. Therefore, in this paper, we propose a normalization method using vector-space model. For each gene/protein name, we rank identifiers using vector-space model, and find the most similar identifier with the name. Experimental result shows the proposed method has 70.7% f-measure.