融合知识图谱与深度学习的药物发现方法

doi:10.16451/j.cnki.issn1003-6059.201812005

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Abstract The massive growing amount of biomedical literature brings huge challenges for data mining. In this paper, a method combining knowledge graph and deep learning is proposed to discover potential therapeutic drugs for disease of interest. Firstly, a biomedical knowledge graph is constructed with the relations extracted from biomedical literature. Then, the entities and relations of the knowledge graph are converted into low dimension continuous embeddings by knowledge graph embedding method. Finally, a recurrent neural network based drug discovery model is trained by using the known drug-disease related associations. The experimental results show that the proposed method can discover drugs for diseases and provide the drug mechanism of action.

Key words： Data Mining Biomedical Knowledge Graph Deep Learning Recurrent Neural Network

Received: 25 October 2018

ZTFLH:

TP 391

Fund:Supported by National Key Research and Development Project of China(No.2016YFC0901902), National Natural Science Foundation of China(No.61272373)

About author:: (SANG Shengtian, Ph.D. candidate. His re-search interests include data mining and lite-rature-based discovery.)
(YANG Zhihao(Corresponding author), Ph.D., professor. His research interests include natural language processing and data mining.)
(LIU Xiaoxia, Ph.D. candidate. Her research interests include data mining and natural language processing.)
(WANG Lei, Ph.D., professor. Her research interests include biomedical informa-tics.)
(ZHAO Di, Ph.D. candidate. His research interests include data mining and natural language processing.)
(LIN Hongfei, Ph.D., professor. His research interests include social media data mining and artificial intelligence.)
(WANG Jian, Ph.D., professor. Her research interests include natural language processing.)

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	SANG Shengtian
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	LIN Hongfei
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Cite this article:

SANG Shengtian,YANG Zhihao,LIU Xiaoxia等. A Method Combining Knowledge Graph and Deep Learning for Drug Discovery[J]. , 2018, 31(12): 1103-1110.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201812005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I12/1103

[1] MORGAN S, GROOTENDORST P, LEXCHIN J, et al. The Cost of Drug Development: A Systematic Review. Health Policy, 2011, 100(1): 4-17.
[2] SPANGLER S, WILKINS A D, BACHMAN B J, et al. Automated Hypothesis Generation Based on Mining Scientific Literature // Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2014: 1877-1886.
[3] COHEN T, WIDDOWS D, SCHVANEVELDT R W, et al. Discovery at a Distance: Farther Journeys in Predication Space // Proc of the IEEE Conference on Bioinformatics and Biomedicine Workshops. Washington, USA: IEEE, 2012: 218-225.
[4] AHLERS C B, HRISTOVSKI D, KILICOGLU H, et al. Using the Literature-Based Discovery Paradigm to Investigate Drug Mechanisms[C/OL]. [2018-09-25]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655783/pdf/amia-0006-s2007.pdf.
[5] HRISTOVSKI D, FRIEDMAN C, RINDFLESCH T C, et al. Exploiting Semantic Relations for Literature-Based Discovery[C/OL]. [2018-09-25]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839258/pdf/AMIA2006_0349.pdf.
[6] GAWEHN E, HISS J A, SCHNEIDER G. Deep Learning in Drug Discovery. Molecular Informatics, 2016, 35(1): 3-14.
[7] RINDFLESCH T C, FISZMAN M. The Interaction of Domain Know-ledge and Linguistic Structure in Natural Language Processing: Interpreting Hypernymic Propositions in Biomedical Text. Journal of Biomedical Informatics, 2003, 36(6): 462-477.
[8] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating Embeddings for Modeling Multi-relational Data // BURGES C J C, BOTTOU L, WELLING M , et al., eds. Advances in Neural Information Processing Systems 26. Cambridge, USA: The MIT Press, 2013: 2787-2795.
[9] CAO D S, ZHANG L X, TAN G S, et al. Computational Prediction of Drug Target Interactions Using Chemical, Biological, and Network Features. Molecular informatics, 2014, 33(10): 669-681.
[10] BYVATOV E, FECHNER U, SADOWSKI J, et al. Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification. Journal of Chemical Information and Computer Sciences, 2003, 43(6): 1882-1889.
[11] CHEN X, LIU M X, YAN G Y. Drug-Target Interaction Prediction by Random Walk on the Heterogeneous Network. Molecular BioSystems, 2012, 8(7): 1970-1978.
[12] LIU H, SONG Y L, GUAN J H, et al. Inferring New Indications for Approved Drugs via Random Walk on Drug-Disease Heterogenous Networks[C/OL]. [2018-09-25]. https://doi.org/10.1186/s12859-016-1336-7.
[13] SANG S, YANG Z, WANG L, et al. Sematyp: A Knowledge Graph Based Literature Mining Method for Drug Discovery[C/OL]. [2018-09-25]. https://doi.org/10.1186/s12859-018-2167-5.