A dictionary-based approach to normalizing gene names in one domain of knowledge from the biomedical literature

Purpose - Gene term variation is a shortcoming in text-mining applications based on biomedical literature-based knowledge discovery. The purpose of this paper is to propose a technique for normalizing gene names in biomedical literature. Design/methodology/approach - Under this proposal, the normalized forms can be characterized as a unique gene symbol, defined as the official symbol or normalized name. The unification method involves five stages: collection of the gene term, using the resources provided by the Entrez Gene database; encoding of gene-naming terms in a table or binary matrix; design of a parametrized finite-state graph (P-FSG); automatic generation of a dictionary; and matching based on dictionary look-up to transform the gene mentions into the corresponding unified form. Findings - The findings show that the approach yields a high percentage of recall. Precision is only moderately high, basically due to ambiguity problems between gene-naming terms and words and abbreviations in general English. Research limitations/implications - The major limitation of this study is that biomedical abstracts were analyzed instead of full-text documents. The number of under-normalization and over-normalization errors is reduced considerably by limiting the realm of application to biomedical abstracts in a well-defined domain. Practical implications - The system can be used for practical tasks in biomedical literature mining. Normalized gene terms can be used as input to literature-based gene clustering algorithms, for identifying hidden gene-to-disease, gene-to-gene and gene-to-literature relationships. Originality/value - Few systems for gene term variation handling have been developed to date. The technique described performs gene name normalization by dictionary look-up.