title: BMExpert: Mining MEDLINE for Finding Experts in Biomedical Domains Based on Language Model.
abstract: With the rapid development of biomedical sciences, a great number of documents have been published to report new scientific findings and advance the process of knowledge discovery. By the end of 2013, the largest biomedical literature database, MEDLINE, has indexed over 23 million abstracts. It is thus not easy for scientific professionals to find experts on a certain topic in the biomedical domain. In contrast to the existing services that use some ad hoc approaches, we developed a novel solution to biomedical expert finding, BMExpert, based on the language model. For finding biomedical experts, who are the most relevant to a specific topic query, BMExpert mines MEDLINE documents by considering three important factors: relevance of documents to the query topic, importance of documents, and associations between documents and experts. The performance of BMExpert was evaluated on a benchmark dataset, which was built by collecting the program committee members of ISMB in the past three years (2012-2014) on 14 different topics. Experimental results show that BMExpert outperformed three existing biomedical expert finding services: JANE, GoPubMed, and eTBLAST, with respect to both MAP (mean average precision) and P@50 (Precision). BMExpert is freely accessed at http://datamining-iip.fudan.edu.cn/service/BMExpert/.
authors.forename: Beichen
authors.initials: B
authors.lastname: Wang
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Chen
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Mamitsuka
authors.forename: Shanfeng
authors.initials: S
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D005104:Expert Testimony; D007802:Language; D016239:MEDLINE; D000069550:Machine Learning; D008373:Manuscripts as Topic; D008962:Models, Theoretical; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*