Normalization of gene/protein names in biological literatures using Vector-Space Model.

ABSTRACT

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 create 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.