Learning string similarity measures for gene/protein name dictionary look-up using logistic regression | Zendy

Yoshimasa Tsuruoka | Zendy; John McNaught | Zendy; Jun'i chi Tsujii | Zendy; Sophia Ananiadou | Zendy

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Learning string similarity measures for gene/protein name dictionary look-up using logistic regression

Author(s) -

Yoshimasa Tsuruoka,

John McNaught,

Jun'i chi Tsujii,

Sophia Ananiadou

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm393

Subject(s) - similarity (geometry) , string (physics) , matching (statistics) , computer science , artificial intelligence , measure (data warehouse) , string searching algorithm , string metric , similarity measure , logistic regression , data mining , pattern recognition (psychology) , natural language processing , machine learning , pattern matching , mathematics , statistics , mathematical physics , image (mathematics)

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.

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