Abstract
A large amount of valuable knowledge is hidden in the vast biomedical literatures, publications, and online contents. In order to identify the previously unknown biomedical knowledge from these resources, we propose a new method of knowledge discovery based on SPO predications, which constructs a three-level SPO-semantic relation network in the considered area. We carry out the experiments in the area of induced pluripotent stem cells, and the experimental results indicate that our proposed method can significantly discover the potential biomedical knowledge in this area, and the performance analysis of this method sheds lights on the ways to further improvements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cairelli, M.J., Fiszman, M., Zhang, H., Rindflesch, T.C.: Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury. J. Biomed. Semant. 6(25), 1–14 (2015)
Fiszman, M., Rindflesch, T.C., Kilicoglu, H.: Abstraction summarization for managing the biomedical research literature. In: Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics, pp. 76–83 (2004)
Keselman, A., Rosemblat, G., Kilicoglu, H.: Adapting semantic natural language processing technology to address information overload in influenza epidemic management. J. Am. Soc. Info. Sci. Technol. 61(12), 2531–2543 (1990)
Lü, Z.P., Huang, W.Q.: Iterated tabu search for identifying community structure in complex networks. Phys. Rev. E 80, 026130 (2009)
Rather, N.N., Chintan, O.P., Khan, S.A.: Using deep learning towards biomedical knowledge discovery. I. J. Math. Sci. Comput. 2, 1–10 (2017)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Nikdelfaz, O., Jalili, S.: Disease genes prediction by HMM based PU-learning using gene expression profiles. J. Biomed. Inform. 81, 102–111 (2018)
Swanson, D.R.: Medical literature as a potential source of new knowledge. Bull. Med. Libr. Assoc. 78(1), 29–37 (1990)
Wei, L., Hu, Z.Y., Pang, H.S., Qin, X.C., Guo, H.M., Fang, S.: Study on knowledge discovery in biomedical literature based on spo predications: a case study of induced pluripotent stem cells. Digit. Libr. Forum 9, 28–34 (2017)
Workman, T.E., Fiszman, M., Hurdle, J.F., Rindflesch, T.C.: Biomedical text summarization to support genetic database curation: using semantic medline to create a secondary database of genetic information. J. Med. Libr. Assoc. 98(4), 273–281 (2010)
Yousef, A., Charkari, N.M.: SFM: a novel sequence-based fusion method for disease genes identification and prioritization. J. Theor. Biol. 383, 12–19 (2015)
Zhang, H., Fiszman, M., Shin, D., Miller, C.M., Rosemblat, G., Rindflesch, T.C.: Degree centrality for semantic abstraction summarization of therapeutic studies. J. Biomed. Inform. 44, 830–838 (2011)
Zhang, Y., Lin, H., Yang, Z., Wang, J., Zhang, S., Sun, Y., Yang, L.: A hybrid model based on neural networks for biomedical relation extraction. J. Biomed. Inform. 81, 83–92 (2018)
Acknowledgments
The work in this paper was supported by the key projects of the National Social Science Foundation of China “Theory and Applications Research of Subject-Informatics for Domain Knowledge Discovery” (Grant No: 17ATQ008), supported by the Informationization Special Project of Chinese Academy of Sciences “E-Science Application for Knowledge Discovery in Stem Cells” (Grant No: XXH13506-203), and supported by the Fundamental Research Funds for the Central Universities (Grant No. A0920502051722-53).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hu, ZY., Zeng, RQ., Qin, XC., Wei, L., Zhang, Z. (2018). A Method of Biomedical Knowledge Discovery by Literature Mining Based on SPO Predications: A Case Study of Induced Pluripotent Stem Cells. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-96133-0_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96132-3
Online ISBN: 978-3-319-96133-0
eBook Packages: Computer ScienceComputer Science (R0)