title: Link Prediction on a Network of Co-occurring MeSH Terms: Towards Literature-based Discovery.
abstract: OBJECTIVE
  Literature-based discovery (LBD) is a text mining methodology for automatically generating research hypotheses from existing knowledge. We mimic the process of LBD as a classification problem on a graph of MeSH terms. We employ unsupervised and supervised link prediction methods for predicting previously unknown connections between biomedical concepts.
  
  
  METHODS
  We evaluate the effectiveness of link prediction through a series of experiments using a MeSH network that contains the history of link formation between biomedical concepts. We performed link prediction using proximity measures, such as common neighbor (CN), Jaccard coefficient (JC), Adamic / Adar index (AA) and preferential attachment (PA). Our approach relies on the assumption that similar nodes are more likely to establish a link in the future.
  
  
  RESULTS
  Applying an unsupervised approach, the AA measure achieved the best performance in terms of area under the ROC curve (AUC = 0.76), followed by CN, JC, and PA. In a supervised approach, we evaluate whether proximity measures can be combined to define a model of link formation across all four predictors. We applied various classifiers, including decision trees, k-nearest neighbors, logistic regression, multilayer perceptron, naïve Bayes, and random forests. Random forest classifier accomplishes the best performance (AUC = 0.87).
  
  
  CONCLUSIONS
  The link prediction approach proved to be effective for LBD processing. Supervised statistical learning approaches clearly outperform an unsupervised approach to link prediction.
authors.affiliation: Andrej Kastrin, PhD, Faculty of Information Studies, Ljubljanska cesta 31A, SI-8000 Novo Mesto, Slovenia, E-mail: andrej.kastrin@guest.arnes.si.
authors.forename: Andrej
authors.initials: A
authors.lastname: Kastrin
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Rindflesch
authors.forename: Dimitar
authors.initials: D
authors.lastname: Hristovski
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D057225:Data Mining; D063369:Knowledge Discovery; D046650:Medical Subject Headings
keywords: Complex networks; link prediction; literature-based discovery; network analysis