title: Link prediction in a MeSH co-occurrence network: preliminary results.
abstract: Literature-based discovery (LBD) refers to automatic discovery of implicit relations from the scientific literature. Co-occurrence associations between biomedical concepts are commonly used in LBD. These co-occurrences can be represented as a network that consists of a set of nodes representing concepts and a set of edges representing their relationships (or links). In this paper we propose and evaluate a methodology for link prediction of implicit connections in a network of co-occurring Medical Subject Headings (MeSH®). The proposed approach is complementary to, and may augment, existing LBD methods. Link prediction was performed using Jaccard and Adamic-Adar similarity measures. The preliminary results showed high prediction performance, with area under the ROC curve of 0.78 and 0.82 for the two similarity measures, respectively.
authors.affiliation: Faculty of Information Studies, Novo mesto, Slovenia.
authors.forename: Andrej
authors.initials: A
authors.lastname: Kastrin
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, USA.
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Rindflesch
authors.affiliation: Institute of Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
authors.forename: Dimitar
authors.initials: D
authors.lastname: Hristovski
mesh_terms: D001185:Artificial Intelligence; D016239:MEDLINE; D046650:Medical Subject Headings; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D010865:Pilot Projects; D012660:Semantics; D009626:Terminology as Topic