Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature

Chemical compounds (drugs) and diseases are among top searched keywords on the PubMed database of biomedical literature by biomedical researchers all over the world (according to a study in 2009). Working with PubMed is essential for researchers to get insights into drugs’ side effects (chemical-induced disease relations (CDR), which is essential for drug safety and toxicity. It is, however, a catastrophic burden for them as PubMed is a huge database of unstructured texts, growing steadily very fast (~28 millions scientific articles currently, approximately two deposited per minute). As a result, biomedical text mining has been empirically demonstrated its great implications in biomedical research communities. Biomedical text has its own distinct challenging properties, attracting much attetion from natural language processing communities. A large-scale study recently in 2018 showed that incorporating information into indenpendent multiple-input layers outperforms concatenating them into a single input layer (for biLSTM), producing better performance when compared to state-of-the-art CDR classifying models. This paper demonstrates that for a CNN it is vice-versa, in which concatenation is better for CDR classification. To this end, we develop a CNN based model with multiple input concatenated for CDR classification. Experimental results on the benchmark dataset demonstrate its outperformance over other recent state-of-the-art CDR classification models.
 Keywords: 
 Chemical disease relation prediction, Convolutional neural network, Biomedical text mining
 References
 [1] Paul SM, S. Mytelka, C.T. Dunwiddie, C.C. Persinger, B.H. Munos, S.R. Lindborg, A.L. Schacht, How to improve R&D productivity: The pharmaceutical industry's grand challenge, Nat Rev Drug Discov. 9(3) (2010) 203-14. https://doi.org/10.1038/nrd3078.
 [2] J.A. DiMasi, New drug development in the United States from 1963 to 1999, Clinical pharmacology and therapeutics 69 (2001) 286-296. https://doi.org/10.1067/mcp.2001.115132.
 [3] C.P. Adams, V. Van Brantner, Estimating the cost of new drug development: Is it really $802 million? Health Affairs 25 (2006) 420-428. https://doi.org/10.1377/hlthaff.25.2.420.
 [4] R.I. Doğan, G.C. Murray, A. Névéol et al., "Understanding PubMed user search behavior through log analysis", Oxford Database, 2009.
 [5] G.K. Savova, J.J. Masanz, P.V. Ogren et al., "Mayo clinical text analysis and knowledge extraction system (cTAKES): Architecture, component evaluation and applications", Journal of the American Medical Informatics Association, 2010.
 [6] T.C. Wiegers, A.P. Davis, C.J. Mattingly, Collaborative biocuration-text mining development task for document prioritization for curation, Database 22 (2012) pp. bas037.
 [7] N. Kang, B. Singh, C. Bui et al., "Knowledge-based extraction of adverse drug events from biomedical text", BMC Bioinformatics 15, 2014.
 [8] A. Névéol, R.L. Doğan, Z. Lu, "Semi-automatic semantic annotation of PubMed queries: A study on quality, Efficiency, Satisfaction", Journal of Biomedical Informatics 44, 2011.
 [9] L. Hirschman, G.A. Burns, M. Krallinger, C. Arighi, K.B. Cohen et al., Text mining for the biocuration workflow, Database Apr 18, 2012, pp. bas020.
 [10] Wei et al., "Overview of the BioCreative V Chemical Disease Relation (CDR) Task", Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, 2015.
 [11] P. Verga, E. Strubell, A. McCallum, Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction, In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1 (2018) 872-884.
 [12] Y. Shen, X. Huang, Attention-based convolutional neural network for semantic relation extraction, In: Proceedings of COLING 2016, the Twenty-sixth International Conference on Computational Linguistics: Technical Papers, The COLING 2016 Organizing Committee, Osaka, Japan, 2016, pp. 2526-2536.
 [13] Y. Peng, Z. Lu, Deep learning for extracting protein-protein interactions from biomedical literature, In: Proceedings of the BioNLP 2017 Workshop, Association for Computational Linguistics, Vancouver, Canada, 2016, pp. 29-38.
 [14] S. Liu, F. Shen, R. Komandur Elayavilli, Y. Wang, M. Rastegar-Mojarad, V. Chaudhary, H. Liu, Extracting chemical-protein relations using attention-based neural networks, Database, 2018.
 [15] H. Zhou, H. Deng, L. Chen, Y. Yang, C. Jia, D. Huang, Exploiting syntactic and semantics information for chemical-disease relation extraction, Database, 2016, pp. baw048.
 [16] S. Liu, B. Tang, Q. Chen et al., Drug–drug interaction extraction via convolutional neural networks, Comput, Math, Methods Med, Vol (2016) 1-8. https://doi.org/10.1155/2016/6918381.
 [17] L. Wang, Z. Cao, G. De Meloet al., Relation classification via multi-level attention CNNs, In: Proceedings of the Fifty-fourth Annual Meeting of the Association for Computational Linguistics 1 (2016) 1298-1307. 
 https://doi.org/10.18653/v1/P16-1123.
 [18] J. Gu, F. Sun, L. Qian et al., Chemical-induced disease relation extraction via convolutional neural network, Database (2017) 1-12. https://doi.org/10.1093/database/bax024.
 [19] H.Q. Le, D.C. Can, S.T. Vu, T.H. Dang, M.T. Pilehvar, N. Collier, Large-scale Exploration of Neural Relation Classification Architectures, In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, pp. 2266-2277.
 [20] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, In Proceedings of the IEEE. 86(11) (1998) 2278-2324.
 [21] Y. Kim, Convolutional neural networks for sentence classification, ArXiv preprint arXiv:1408.5882.
 [22] C. Nagesh, Panyam, Karin Verspoor, Trevor Cohn and Kotagiri Ramamohanarao, Exploiting graph kernels for high performance biomedical relation extraction, Journal of biomedical semantics 9(1) (2018) 7.
 [23] H. Zhou, H. Deng, L. Chen, Y. Yang, C. Jia, D. Huang, Exploiting syntactic and semantics information for chemical-disease relation extraction, Database, 2016.

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