title: Multistage gene normalization and SVM-based ranking for protein interactor extraction in full-text articles.
abstract: The interactor normalization task (INT) is to identify genes that play the interactor role in protein-protein interactions (PPIs), to map these genes to unique IDs, and to rank them according to their normalized confidence. INT has two subtasks: gene normalization (GN) and interactor ranking. The main difficulties of INT GN are identifying genes across species and using full papers instead of abstracts. To tackle these problems, we developed a multistage GN algorithm and a ranking method, which exploit information in different parts of a paper. Our system achieved a promising AUC of 0.43471. Using the multistage GN algorithm, we have been able to improve system performance (AUC) by 1.719 percent compared to a one-stage GN algorithm. Our experimental results also show that with full text, versus abstract only, INT AUC performance was 22.6 percent higher.
authors.affiliation: Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, ROC. hongjie@iis.sinica.edu.tw
authors.forename: Hong-Jie
authors.initials: HJ
authors.lastname: Dai
authors.forename: Po-Ting
authors.initials: PT
authors.lastname: Lai
authors.forename: Richard Tzong-Han
authors.initials: RT
authors.lastname: Tsai
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D005796:Genes; D016247:Information Storage and Retrieval; D009323:Natural Language Processing; D010506:Periodicals as Topic; D025941:Protein Interaction Mapping / Q000379:methods*