have used metrics popular in Information Retrieval which include Precision, Recall, Area Under the Curve (AUC), Precision at k, Mean Average Precision (MAP) and others. The approach of Proposing new discoveries or treatments goes beyond replicating past discoveries or predicting time-sliced instances of a particular relationship and shows that a system is capable of being used in realistic situations. This is usually accompanied by peer-reviewed publication in the domain or vetting by a domain expert. Text mining The automation of literature-based discovery relies heavily on text mining. The language in scientific articles often include ambiguities, and an important step for coeherent parsing of the literature is the extraction of the sense of each term in the context they are used, a task called Word-sense disambiguation (WSD). For example, terms for genes like CT (PCYT1A) called and MR (NR3C2) can be confused with the acronyms for Computational Tomography and Magnetic Resonance, requiring sofisticated disambiguation systems. Terms are often reconciled to ontologies or other sources of unique identifiers, such as the Unified Medical Language System (UMLS). This process of mapping multiple different utterances to a single name or identifier is called normalization. Usage Life sciences LBD has already been used in different waysto identify new connections between biomedical entities and new candidate genes and treatments for illnesses. Drug discovery LBD has seen use in drug development and repurposing as well as predicting adverse drug reactions. The method of literature-based discovery has been used to search for treatments for a number of human diseases, including: diabetic retinopathy dilated cardiomyopathy Parkinson's disease prostate cancer gastric cancer multiple sclerosis Gene and protein function discovery The approach has also been used to propose relations of genes with particular diseases, like breast cancer. In the context of systems vaccinology, it was used to identify proteins related to interferon gamma and that play a role in the response to vaccines. It has also been used to propose mechanisms for currently used drugs. Biomarker discovery LBD has been explored as a tool to identify biomarkers for diagnostic and prognostic for diseases, e.g. for the risk of type 2 diabetes. Other uses Besides providing scientific hypotheses about the world, LBD has also been used to improve data analysis, via the automatic identification of possible confounding factors using the medical literature. It has also been used to understand better disease etiology and the relation of different diseases, for example looking for