Journal
BIOINFORMATICS
Volume 23, Issue 20, Pages 2768-2774Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btm393
Keywords
-
Categories
Funding
- BBSRC [BB/E004431/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/E004431/1] Funding Source: researchfish
- Biotechnology and Biological Sciences Research Council [BB/E004431/1] Funding Source: Medline
Ask authors/readers for more resources
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-up tasks.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available