Prioritizing adverse drug reaction and drug repositioning candidates generated by literature-based discovery

Literature based discovery (LBD) is a well-known paradigm to discover hidden knowledge in scientific literature. By identifying and utilizing reported findings in literature, LBD hypothesizes novel discoveries. Most often, LBD systems generate a long list of potential discoveries and it would be time consuming and expensive to validate all of those discoveries. Preliminary validation or prioritization of the discoveries can improve the significance of LBD systems. In this study, we proposed a method utilizing information surrounding causal findings to prioritize discoveries generated by LBD systems. As a case study, we focused on discovering drug-disease relations, which have potential to identify drug repositioning candidates or adverse drug reactions. Our LBD system used drug-gene and genedisease semantic predication in SemMedDB as causal findings and Swanson's ABC model to generate potential drug-disease relations. Using sentences, which causal findings extracted from, our ranking method trained a binary classifier to classify generated drug-disease relations into desired classes. We trained and tested our classifier for three different purposes: a) drug repositioning b) adverse drug events c) drug-disease relation detection. The classifier obtained 0.78, 0.86, and 0.83 f-measure respectively for these tasks. The number of causal findings of each hypothesis, which were classified as positive by the classifier, is the main metric for ranking the hypotheses in the proposed method. To evaluate the ranking method, we counted and compared the number of true relations in the top 100 pairs, which were ranked by our method and one of previous methods. Out of 181 true relations in the test dataset, the proposed method ranked 20 of them in top 100 relations while this number was 13 for the other method.