Abstract
Following the rapid advances of the human microbiome, the importance of micro-organisms especially bacteria is gradually recognized. The interactions among bacteria and their host are particulary important for understanding the mechanism of microbe-relate diseases. This article mainly introduces an explorative study to extract the relations between bacteria and diseases based on biomedical text mining. We have constructed a Microbe-Disease Knowledge Graph (MDKG) through integrating multi-source heterogeneous data from Wikipedia text and other related databases. Specifically, we introduce the word embedding obtained from biomedical literature into traditional method. Results show that the pre-trained relation vectors can better represent the real associations between entities. Therefore, the construction of MDKG can also provide a new way to predict and analyse the associations between microbes and diseases based on text mining.
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Acknowledgement
This research is supported by National Key Research and Development Program of China (2017YFC0909502) and the National Natural Science Foundation of China (61532008 and 61872157). We also thanks to the support of Fundamental Research Funds for Central Universities (CCNU19ZN009).
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Fu, C., Zhong, R., Jiang, X., He, T., Jiang, X. (2020). An Integrated Knowledge Graph for Microbe-Disease Associations. In: Huang, Z., Siuly, S., Wang, H., Zhou, R., Zhang, Y. (eds) Health Information Science. HIS 2020. Lecture Notes in Computer Science(), vol 12435. Springer, Cham. https://doi.org/10.1007/978-3-030-61951-0_8
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DOI: https://doi.org/10.1007/978-3-030-61951-0_8
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