title: Entitymetrics: measuring the impact of entities.
abstract: This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.
authors.affiliation: Department of Information and Library Science, Indiana University, Bloomington, Indiana, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ding
authors.forename: Min
authors.initials: M
authors.lastname: Song
authors.forename: Jia
authors.initials: J
authors.lastname: Han
authors.forename: Qi
authors.initials: Q
authors.lastname: Yu
authors.forename: Erjia
authors.initials: E
authors.lastname: Yan
authors.forename: Lili
authors.initials: L
authors.lastname: Lin
authors.forename: Tamy
authors.initials: T
authors.lastname: Chambers
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015706:Bibliometrics; D001699:Biometry / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D019359:Knowledge; D008687:Metformin; D016571:Neural Networks, Computer; D043922:Toxicogenetics / Q000379:methods
chemical_list: D008687:Metformin