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

@article{citeulike:1626506, abstract = {MOTIVATION: 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 regression-based similarity measure outperforms existing similarity measures in dictionary look-uptasks. AVAILABILITY: A dictionary look-up system using the similarity measures described in this paper is available at http://text0.mib.man.ac.uk/software/mldic/ CONTACT: yoshimasa.tsuruoka@manchester.ac.uk.}, address = {School of Computer Science, The University of Manchester, UK.}, author = {Tsuruoka, Yoshimasa and McNaught, John and Tsujii, Jun'ichi and Ananiadou, Sophia }, citeulike-article-id = {1626506}, doi = {10.1093/bioinformatics/btm393}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {bionlp, gene, name, normalization}, month = {August}, posted-at = {2007-10-31 13:28:12}, priority = {4}, title = {Learning string similarity measures for gene/protein name dictionary look-up using logistic regression.}, url = {http://dx.doi.org/10.1093/bioinformatics/btm393}, year = {2007} }

TY - JOUR ID - citeulike:1626506 L3 - citeulike-article-id:1626506 TI - Learning string similarity measures for gene/protein name dictionary look-up using logistic regression. JF - Bioinformatics AD - School of Computer Science, The University of Manchester, UK. SN - 1460-2059 N2 - MOTIVATION: 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 regression-based similarity measure outperforms existing similarity measures in dictionary look-uptasks. AVAILABILITY: A dictionary look-up system using the similarity measures described in this paper is available at http://text0.mib.man.ac.uk/software/mldic/ CONTACT: yoshimasa.tsuruoka@manchester.ac.uk. KW - bionlp KW - gene KW - name KW - normalization AU - Tsuruoka, Yoshimasa AU - McNaught, John AU - Tsujii, Jun'ichi AU - Ananiadou, Sophia PY - 2007/08/12/ UR - http://dx.doi.org/10.1093/bioinformatics/btm393 ER -