To extract relationships with good precision. To complicate matters further, authors frequently speculate about potential

To extract relationships with good precision. To complicate matters further, authors frequently speculate about potential relationships (eg APPL may interact with Akt2). These statements do not correspond to the definition of a relationship, but that the relationship is proposed to exist. It is important to identify these speculative statements67 and prevent them from biasing any downstream analyses. For the same reason, it is equally important to detect the negation of relationships68 (eg APPL does not interact with Akt2).Hypothesis generationThe scientific literature not only contains explicit knowledge, such as `APPL interacts with Akt2′, but also implicit knowledge,69 such as hidden refutations or qualifications, inferences from transitive relations, hidden or unrecognised analogies and the accumulation of weak tests (which could be used in meta-analyses). Swanson’s serendipitous discovery of the connection between Raynaud’s disease and fish oil70 is an example of performing an inference on a transitive relation to generate a novel and testable hypothesis. By reading two disjoint sets of literature (no articles are in common, and the articles in one set do not cite or mention articles in the other set), he observed that blood factors were a common theme in both the Raynaud’s disease and# HENRY STEWART PUBLICATIONS 1479 ?364. HUMAN GENOMICS. VOL 5. NO 1. 17 ?29 OCTOBERREVIEWHarmston, Filsell and Stumpfthe fish-oil literature. This led him to propose that fish oil could be used in the treatment of Raynaud’s disease, and the relationship was clinically validated in 1989.71 The discovery led Swanson to propose that `new hypotheses can emerge and scientific discovery can be anticipated or stimulated through the investigation of complementary but disjoint literatures’. This method of literature-based discovery is commonly referred to as Swanson’s ABC model or Swanson Linking, with the hypotheses and new knowledge being described as undiscovered public knowledge. Although the model has mainly been used within the biomedical and biological fields it has also been applied to the humanities literature and the WWW (see Table 4). Mendeleev’s discovery of the law of periodicity and the development of the periodic table can be considered an early example of literature-based discovery (LBD), as it was: `a direct outcome of the stock of generalisations and established facts which has accumulated by the end of the decade 1860?1870.’ The information required to build the table of elements had already been published, but it hadnever been analysed as a whole.72 More recently, Hettne et al. 73 combined TM with network analysis in order to generate new mechanistic hypotheses relating to the complex regional pain syndrome (CRPS). NF-kB was identified as potentially being involved by first extracting genes relating to CRPS from the literature and then investigating potential links between these genes which were not mentioned in the CRPS literature. This hypothesis has led to Sodium lasalocid molecular weight several new ideas regarding the aetiology of the disease and the proposal of a novel drug target. By exploiting the context of protein mentions, van Haagen et al. 74 were able to predict a novel interaction between CAPN3 and PARVB. Integrating information extracted from the literature with microarray experiments has led to PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/27484364 the proposition of a relationship between SIP and the invasiveness of glioblastoma cell lines.75 All of this work shows the potential for TM to generate testable hy.