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Title: BMExpert: Mining MEDLINE for Finding Experts in Biomedical Domains Based on Language Model
Authors: Wang, Beichen
Chen, Xiaodong
Mamitsuka, Hiroshi  kyouindb  KAKEN_id  orcid (unconfirmed)
Zhu, Shanfeng
Author's alias: 馬見塚, 拓
Keywords: information retrieval
Biomedical text mining
expert finding
language model
Issue Date: 6-May-2015
Publisher: Institute of Electrical and Electronics Engineers Inc. (IEEE)
Journal title: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume: 12
Issue: 6
Start page: 1286
End page: 1294
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
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
DOI(Published Version): 10.1109/TCBB.2015.2430338
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