Using statistical and knowledge-based approaches for literature-based discovery.

Abstract

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 the literature. In this paper, we present a literature-based discovery system called LitLinker that incorporates knowledge-based methodologies with a statistical method to mine the biomedical literature for new, potentially causal connections between biomedical terms. We demonstrate LitLinker's ability to capture novel and interesting connections between diseases and chemicals, drugs, genes, or molecular sequences from the published biomedical literature. We also evaluate LitLinker's performance by using the information retrieval metrics of precision and recall.