title: BEST: Next-Generation Biomedical Entity Search Tool for Knowledge Discovery from Biomedical Literature.
abstract: As the volume of publications rapidly increases, searching for relevant information from the literature becomes more challenging. To complement standard search engines such as PubMed, it is desirable to have an advanced search tool that directly returns relevant biomedical entities such as targets, drugs, and mutations rather than a long list of articles. Some existing tools submit a query to PubMed and process retrieved abstracts to extract information at query time, resulting in a slow response time and limited coverage of only a fraction of the PubMed corpus. Other tools preprocess the PubMed corpus to speed up the response time; however, they are not constantly updated, and thus produce outdated results. Further, most existing tools cannot process sophisticated queries such as searches for mutations that co-occur with query terms in the literature. To address these problems, we introduce BEST, a biomedical entity search tool. BEST returns, as a result, a list of 10 different types of biomedical entities including genes, diseases, drugs, targets, transcription factors, miRNAs, and mutations that are relevant to a user's query. To the best of our knowledge, BEST is the only system that processes free text queries and returns up-to-date results in real time including mutation information in the results. BEST is freely accessible at http://best.korea.ac.kr.
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Sunwon
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Donghyeon
authors.initials: D
authors.lastname: Kim
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Kyubum
authors.initials: K
authors.lastname: Lee
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Jaehoon
authors.initials: J
authors.lastname: Choi
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Seongsoon
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Minji
authors.initials: M
authors.lastname: Jeon
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Sangrak
authors.initials: S
authors.lastname: Lim
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Donghee
authors.initials: D
authors.lastname: Choi
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Sunkyu
authors.initials: S
authors.lastname: Kim
authors.affiliation: Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America.
authors.forename: Aik-Choon
authors.initials: AC
authors.lastname: Tan
authors.affiliation: Department of Computer Science and Engineering, Korea University, Seoul, Korea.
authors.forename: Jaewoo
authors.identifier: http://orcid.org/0000-0001-6798-9106
authors.initials: J
authors.lastname: Kang
mesh_terms: D035843:Biomedical Research; D057225:Data Mining / Q000379:methods*; D004351:Drug Resistance / Q000235:genetics; D009154:Mutation; D011642:Publications; D014584:User-Computer Interface