title: Biomedical knowledge navigation by literature clustering.
abstract: There is an urgent need for a system that facilitates surveys by biomedical researchers and the subsequent formulation of hypotheses based on the knowledge stored in literature. One approach is to cluster papers discussing a topic of interest and reveal its sub-topics that allow researchers to acquire an overview of the topic. We developed such a system called McSyBi. It accepts a set of citation data retrieved with PubMed and hierarchically and non-hierarchically clusters them based on the titles and the abstracts using statistical and natural language processing methods. A novel point is that McSyBi allows its users to change the clustering by entering a MeSH term or UMLS Semantic Type, and therefore they can see a set of citation data from multiple aspects. We evaluated McSyBi quantitatively and qualitatively: clustering of 27 sets of citation data (40643 different papers) and scrutiny of several resultant clusters. While non-hierarchical clustering provides us with an overview of the target topic, hierarchical clustering allows us to see more details and relationships among citation data. McSyBi is freely available at http://textlens.hgc.jp/McSyBi/.
authors.affiliation: Department of Computational Biology, University of Tokyo, Kibanto CB01, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. yayamamo@hgc.jp <yayamamo@hgc.jp>
authors.forename: Yasunori
authors.initials: Y
authors.lastname: Yamamoto
authors.forename: Toshihisa
authors.initials: T
authors.lastname: Takagi
mesh_terms: D001185:Artificial Intelligence; D001695:Biology / Q000379:methods; D016000:Cluster Analysis; D003628:Database Management Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D008511:Medicine / Q000379:methods; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D010506:Periodicals as Topic; D039781:PubMed