ZK DrugResist 2.0: A TextMiner to extract semantic relations of drug resistance from PubMed

Khalid, Zoya and Sezerman, O. Uğur (2017) ZK DrugResist 2.0: A TextMiner to extract semantic relations of drug resistance from PubMed. Journal of Biomedical Informatics, 69 . pp. 93-98. ISSN 1532-0464 (Print) 1532-0480 (Online)

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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.
Item Type: Article
Uncontrolled Keywords: Drug resistance; Hybrid approach; Machine learning; NLP; Relation extraction; Rule based methods
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Biological Sciences & Bio Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Zoya Khalid
Date Deposited: 18 May 2017 12:16
Last Modified: 18 May 2017 12:16
URI: https://research.sabanciuniv.edu/id/eprint/32228

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