title: A machine learning-based system to normalise gene mentions to unique database identifiers.
abstract: In this paper, we propose an integrated Gene normaliser (GNer) to assign a unique database identifier for each recognised gene mention in biological literature. The GNer combines Support Vector Machines (SVMs) and some rule-base components. First, we construct a dictionary from EntrezGene and BioThesaurus. Then we reduce variations and ambiguities of synonyms based on a designed pre-processor. Finally, a SVM-based disambiguation filter is developed to eliminate the ambiguity of exact matching. From the experimental results, the proposed GNer can achieve a fairly good performance, which can achieve the precision 80.5%, the recall 86.4% and the F(beta=1) measure 83.4.
authors.affiliation: School of Information Sciences, Nanjing Audit University, Nanjing 211815, China. yifeichen91@nau.edu.cn
authors.forename: Yifei
authors.initials: Y
authors.lastname: Chen
authors.forename: Feng
authors.initials: F
authors.lastname: Liu
authors.forename: Bernard
authors.initials: B
authors.lastname: Manderick
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D030541:Databases, Genetic; D005796:Genes; D010363:Pattern Recognition, Automated / Q000379:methods; D060388:Support Vector Machine