ABSTRACT One of the bottlenecks of biomedical data integration is variation of terms. Exact string matching often fails to associate a name with its biological concept, i.e. ID or accession number in the database, due to seemingly small differences of names. Soft string matching potentially enables us to find the relevant ID by considering the similarity between the names. However, the accuracy of soft matching highly depends on the similarity measure employed. Results: We used logistic regression for learning a string similarity measure from a dictionary. Experiments using several large-scale gene/protein name dictionaries showed that the logistic regressionbased similarity measure outperforms existing similarity measures in dictionary look-up tasks. Availability: A dictionary look-up system using the similarity measures described in this paper is available at
Tsuruoka et al. REF used logistic regression to learn a string similarity measure from a dictionary, useful for soft-string matching.
Learning string similarity measures for gene / protein name dictionary look-up using logistic regression
Bioinformatics
Bioinformatics
Computer Science Medicine