Title: A corpus-oriented perspective on terminologies of side effect and adverse reaction in support of text retrieval for drug repurposing

Authors: Alex Chengyu Fang; Yemao Liu; Yaping Lu; Jing Cao; Jingbo Xia

Addresses: Department of Linguistics and Translation, City University of Hong Kong, Kowloon, Hong Kong, China ' Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Hongshan, 430070, Wuhan, China ' Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Hongshan, 430070, Wuhan, China ' College of Foreign Languages, Zhongnan University of Economics and Law, Hongshan, 430073, Wuhan, China ' Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Hongshan, 430070, Wuhan, China

Abstract: Text resource selection is a primary concern for efficient and wide-coverage document processing for the extraction of required bio-medical information, while size control and topic relevance are key issues to ensure high-quality output from the retrieval system. In this study, we analysed terms and terminologies used for adverse reaction and side effect of drugs. Furthermore, we proposed an effective strategy that used an intersection of unions of both 'adverse react' hyponyms and 'side effect' hyponyms to evaluate their semantic relationships, including the similarities and differences, in massive biomedical texts. Our results showed that the hyponyms related to these two superordinates perform differently in their use as signifiers of relevant documents. Our proposed strategy resulted in an optimal trade-off for relevant abstract retrieval, followed with empirical work of drug/gene matching in order to test the proposed strategy. The results also confirmed that the strategy was capable of maintaining a good trade-off between text size and content relevance.

Keywords: terminology; Jacquard similarity coefficient; text retrieval; side effects; adverse reaction.

DOI: 10.1504/IJDMB.2018.097684

International Journal of Data Mining and Bioinformatics, 2018 Vol.21 No.3, pp.269 - 286

Available online: 04 Feb 2019 *

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