EdDB [20], which has been used to support a range of biomedical

EdDB [20], which has been used to support a range of biomedical information Camptothecin management research: identifying novel therapeutic approaches [21], labeling extracted information from clinical text [22],Cairelli et al. Journal of Biomedical Semantics (2015) 6:Page 3 ofliterature-based discovery [23-26], clinical information retrieval for physicians [27], retrieving clinical documents [28], abstraction summarization of biomedical texts [29], biological entity recognition [30], identifying disease candidate genes [31], support for cardiovascular clinical guidelines [32,33], interpreting microarray data [34], extracting research findings from literature [35], and supporting formal models of knowledge representation [36,22].Networks of semantic predicationsAny concept in a set of predications can serve as either subject or object in various relationships. For example, one can imagine the concept Glutamate appearing in many predications similar to the following: Glutamate ASSOCIATED_WITH Traumatic Brain Injury, Glutamate INHIBITS Glutamate Synthase, or Glycine STIMULATES Glutamate. Similarly, any concept can have a set of relationships that include it as either the subject or object. Further, any set of predications can be represented as a network with each concept symbolized as a node and each relationship denoted by an edge (or arc) between the two nodes that represent its subject and object. A network containing the above predications is contained in Figure 1. One of the goals of network theory is to establish significance of a given node or relationship. Degree centrality is based on the number of connections a node has and Zhang et al. [37] have shown that it is effective for identifying nodes in a graph that humans consider important. We have previously applied degree centrality to SemRep generated semantic predications to successfully summarize therapeutic studies [38]. For node (or vertex) v, the degree centrality is calculated by dividing the total number of nodes connected to v, deg(v), by the total number of nodes in the network other than v, n-1: C d ??deg ?n-A simple means of judging the value of a given relationship is the frequency of the relationship, that is, a simple count of how many times it occurs in a given set. When using an automated tool, a single occurrence of a predication is much more susceptible to computational error than a predication with multiple instances. Therefore, a higher frequency may provide more confidence in the validity of the relationship, but at the same time, a high frequency is reflective of an abundance of assertions in the literature which is likely PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/16989806 to be indicative of a well-known fact and may be less desirable for novel discovery.Incorporation of systems medicine, natural language processing, and network theoryThis methodology combines ideas and techniques from systems medicine, natural language processing, and network theory. A network of relationships involving substances is created, but the data source is semantic predications from MEDLINE citations rather than genomic or other large-scale experimental data as have often been used for systems medicine. These semantic predications provide a computable form of the knowledge contained in MEDLINE that includes gene, protein, and metabolite relationships analogous to the experimental data traditionally used in systems medicine, as well as additional types of relations at the organism, system, organ, tissue, cell, and molecular level. Statistical.