Khalid, ZoyaSezerman, Osman Ugur2023-02-212023-02-212017-01-0110.1016/j.jbi.2017.04.002https://hdl.handle.net/11443/1233http://dx.doi.org/10.1016/j.jbi.2017.04.002Extracting useful knowledge from an unstructured textual data is a challenging task for biologists, since biomedical literature is growing exponentially on a daily basis. Building an automated method for such tasks is gaining much attention of researchers. ZK DrugResist is an online tool that automatically extracts mutations and expression changes associated with drug resistance from PubMed. In this study we have extended our tool to include semantic relations extracted from biomedical text covering drug resistance and established a server including both of these features. Our system was tested for three relations, Resistance (R), Intermediate (I) and Susceptible (S) by applying hybrid feature set. From the last few decades the focus has changed to hybrid approaches as it provides better results. In our case this approach combines rule-based methods with machine learning techniques. The results showed 97.67\% accuracy with 96\% precision, recall and F-measure. The results have outperformed the previously existing relation extraction systems thus can facilitate computational analysis of drug resistance against complex diseases and further can be implemented on other areas of biomedicine. (C) 2017 Elsevier Inc. All rights reserved.Relation extractionDrug resistanceMachine learningHybrid approachNLPRule based methodsArticleWOS:000409395900010