Title: Using Statistical and Knowledge-Based Approaches for Literature-Based Discovery

The explosive growth in biomedical literature has made it difficult for researchers to keep up with advancements, even in their own narrow specializations. While researchers formulate new hypotheses to test, it is very important for them to identify connections to their work from other parts of the literature. However, the current volume of information has become a great barrier for this task, and new automated tools are needed to help researchers identify new knowledge that bridges gaps across distinct sections of literature. In this paper, we present a literature-based dis-covery system called LitLinker that incorporates knowledge-based methodologies with a statisti-cal method to mine the biomedical literature for new, potentially causal connections between biomedical terms. We demonstrate LitLinker’s ability to capture novel and interesting connec-tions between diseases and chemicals, drugs, genes, or molecular sequences from the published biomedical literature. We also evaluate LitLinker’s performance by using the information re-trieval metrics of precision and recall