Gene name identification and normalization using a model organism database
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Contrast and variability in gene names
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Adaptive string similarity metrics for biomedical reference resolution
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Unsupervised gene/protein named entity normalization using automatically extracted dictionaries
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Gene ontology annotation as text categorization: An empirical study
Information Processing and Management: an International Journal
Rule-Based Protein Term Identification with Help from Automatic Species Tagging
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
A joint model for normalizing gene and organism mentions in text
WBIE '09 Proceedings of the Workshop on Biomedical Information Extraction
ProNormz - An integrated approach for human proteins and protein kinases normalization
Journal of Biomedical Informatics
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The identification of genes in biomedical text typically consists of two stages: identifying gene mentions and normalization of gene names. We have created an automated process that takes the output of named entity recognition (NER) systems designed to identify genes and normalizes them to standard referents. The system identifies human gene synonyms from online databases to generate an extensive synonym lexicon. The lexicon is then compared to a list of candidate gene mentions using various string transformations that can be applied and chained in a flexible order, followed by exact string matching or approximate string matching. Using a gold standard of MEDLINE abstracts manually tagged and normalized for mentions of human genes, a combined tagging and normalization system achieved 0.669 F-measure (0.718 precision and 0.626 recall) at the mention level, and 0.901 F-measure (0.957 precision and 0.857 recall) at the document level for documents used for tagger training.