title: ZK DrugResist 2.0: A TextMiner to extract semantic relations of drug resistance from PubMed.
abstract: Extracting 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.
authors.affiliation: Department of Biological Sciences and Bioengineering, Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey. Electronic address: zoyakhalid@sabanciuniv.edu.
authors.forename: Zoya
authors.initials: Z
authors.lastname: Khalid
authors.affiliation: Department of Biostatistics and Medical Informatics, Acıbadem University, Istanbul, Turkey. Electronic address: ugur.sezerman@acibadem.edu.tr.
authors.forename: Osman Ugur
authors.initials: OU
authors.lastname: Sezerman
mesh_terms: D004351:Drug Resistance; D006801:Humans; D000069550:Machine Learning; D039781:PubMed; D012660:Semantics
keywords: Drug resistance; Hybrid approach; Machine learning; NLP; Relation extraction; Rule based methods