The strength of co-authorship in gene name disambiguation

Background: A biomedical entity mention in articles and other free texts is often ambiguous. For example, 13% of the gene names ( aliases) might refer to more than one gene. The task of Gene Symbol Disambiguation (GSD) - a special case of Word Sense Disambiguation (WSD) - is to assign a unique gene identifier for all identified gene name aliases in biology-related articles. Supervised and unsupervised machine learning WSD techniques have been applied in the biomedical field with promising results. We examine here the utilisation potential of the fact - one of the special features of biological articles - that the authors of the documents are known through graph-based semi-supervised methods for the GSD task. Results: Our key hypothesis is that a biologist refers to each particular gene by a fixed gene alias and this holds for the co-authors as well. To make use of the co-authorship information we decided to build the inverse co-author graph on MedLine abstracts. The nodes of the inverse co-author graph are articles and there is an edge between two nodes if and only if the two articles have a mutual author. We introduce here two methods using distances ( based on the graph) of abstracts for the GSD task. We found that a disambiguation decision can be made in 85% of cases with an extremely high (99.5%) precision rate just by using information obtained from the inverse coauthor graph. We incorporated the co-authorship information into two GSD systems in order to attain full coverage and in experiments our procedure achieved precision of 94.3%, 98.85%, 96.05% and 99.63% on the human, mouse, fly and yeast GSD evaluation sets, respectively. Conclusion: Based on the promising results obtained so far we suggest that the co-authorship information and the circumstances of the articles' release ( like the title of the journal, the year of publication) can be a crucial building block of any sophisticated similarity measure among biological articles and hence the methods introduced here should be useful for other biomedical natural language processing tasks ( like organism or target disease detection) as well.