Identification of Conclusive Association Entities by Biomedical Association Mining
Conclusive association entities (CAEs) in the title and the abstract of an article a are those biomedical entities (e.g., genes, diseases, and chemicals) that are specific targets on which conclusive findings about their associations are reported in a. Identification of the CAEs is essential for the analysis of conclusive associations, which is a task routinely conducted by many biomedical scientists. However, CAE identification is challenging, as it is difficult to identify the specific entities and then estimate how conclusive the findings on the entities are. In this paper we present an association mining technique to improve CAE identification. The technique is based on a hypothesis: two candidate entities in an article are likely to be CAEs of the article if a strong association between them is mined from a collection of articles. Experimental results show that, by integrating the technique with representative keyword identification indicators, CAE identification can be significantly improved. The results are of technical and practical significance to the indexing, curation, and exploration of conclusive associations reported in biomedical literature.
KeywordsBiomedical literature Conclusive association entity Association mining
This research was supported by Ministry of Science and Technology, Taiwan (grant ID: MOST 107-2221-E-320-004).
- 2.Aronson, A.R.: The MMI Ranking Function (1997). https://ii.nlm.nih.gov/MTI/Details/mmi.shtml. Accessed May 2018
- 4.Davis, A.P., et al.: The comparative toxicogenomics database: update 2017. Nucleic Acids Res. 45(Database issue), D972–D978 (2017)Google Scholar
- 6.Heo, G.E., Kang, K.Y., Song, M.: A flexible text mining system for entity and relation extraction in PubMed. In: Proceedings of DTMBIO 2015 (2015)Google Scholar
- 7.Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of ACM SIGKDD, Edmonton, Alberta, Canada, pp. 133–142 (2002)Google Scholar
- 9.Kwon, K., Choi, C.H., Lee, J., Jeong, J., Cho, W.S.: A graph based representative keywords extraction model from news articles. In: Proceedings of the 2015 International Conference on Big Data Applications and Services, pp. 30–36 (2015)Google Scholar
- 10.Li, L., Liu, S., Qin, M., Wang, Y., Huang, D.: Extracting biomedical event with dual decomposition integrating word embeddings. IEEE/ACM Trans. Comput. Biol. Bioinform. 13(4), 669–677 (2016)Google Scholar
- 13.Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2004)Google Scholar
- 16.PubMed: Algorithm for finding best matching citations in PubMed. https://www.ncbi.nlm.nih.gov/books/NBK3827/#pubmedhelp.Algorithm_for_finding_best_ma. Accessed September 2018
- 18.Thomas, J.R., Bharti, S.K., Babu, K.S.: Automatic keyword extraction for text summarization in e-Newspapers. In: Proceedings of ICIA-16 (2016)Google Scholar
- 20.Tsatsaronis, G., et al.: An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition. BMC Bioinform. 16, 138 (2015)Google Scholar