Link Prediction on the Semantic MEDLINE Network

Retrieving and linking different segments of scientific information into understandable and interpretable knowledge is a challenging task. Literature-based discovery (LBD) is a methodology for automatically generating hypotheses for scientific research by uncovering hidden, previously unknown relationships from existing knowledge (published literature). Semantic MEDLINE is a database consisting of semantic predications extracted from MEDLINE citations. The predications provide a normalized form of the meaning of the text. The associations between the concepts in these predications can be described in terms of a network, consisting of nodes and directed arcs, where the nodes represent biomedical concepts and the arcs represent their semantic relationships. In this paper we propose and evaluate a methodology for link prediction of implicit relationships in the Semantic MEDLINE network. Link prediction was performed using different similarity measures including common neighbors, Jaccard index, and preferential attachment. The proposed approach is complementary to, and may augment, existing LBD approaches. The analyzed network consisted of 231,589 nodes and 10,061,747 directed arcs. The results showed high prediction performance, with the common neighbors method providing the best area under the ROC curve of 0.96.