Gene Name Disambiguation Using Multi-Scope Species Detection.
Species detection is an important topic in the text mining field. According to the importance of the research topics (e.g., species assignment to genes and document focus species detection), some studies are dedicated to an individual topic. However, no researcher to date has discussed species detection as a general problem. Therefore, we developed a multi-scope species detection model to identify the focus species for different scopes (i.e., gene mention, sentence, paragraph, and global scope of the entire article). Species assignment is one of the bottlenecks of gene name disambiguation. In our evaluation, recognizing the focus species of a gene mention in four different scopes improved the gene name disambiguation. We used the species cue words extracted from articles to estimate the relevance between an article and a species. The relevance score was calculated by our proposed entities frequency-augmented invert species frequency (EF-AISF) formula, which represents the importance of an entity to a species. We also defined a relation guide factor (RGF) to normalize the relevance score. Our method not only achieved better performance than previous methods but also can handle the articles that do not specifically mention a species. In the DECA corpus, we outperformed previous studies and obtained an accuracy of 88.22 percent.
Jui-Chen
JC
Hsiao
Chih-Hsuan
CH
Wei
Hung-Yu
HY
Kao
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D005796:Genes / Q000235:genetics*; D006801:Humans; D058977:Molecular Sequence Annotation / Q000145:classification*; D060388:Support Vector Machine