DOI QR코드

DOI QR Code

Knowledge Base Associated with Autism Construction Using CRFs Learning

  • Yang, Ronggen (School of Intelligent Science and Control Engineering, Jinling Institute of Technology) ;
  • Gong, Lejun (School of Computer, Nanjing University of Posts and Telecommunications)
  • Received : 2019.06.26
  • Accepted : 2019.09.03
  • Published : 2019.12.31

Abstract

Knowledge base means a library stored in computer system providing useful information or appropriate solutions to specific area. Knowledge base associated with autism is the complex multidimensional information set related to the disease autism for its pathogenic factor and therapy. This paper focuses on the knowledge of biological molecular information extracted from massive biomedical texts with the aid of widespread used machine learning methods. Six classes of biological molecular information (such as protein, DNA, RNA, cell line, cell component, and cell type) are concerned and the probability statistics method, conditional random fields (CRFs), is utilized to discover these knowledges in this work. The knowledge base can help biologists to etiological analysis and pharmacists to drug development, which can at least answer four questions in question-answering (QA) system, i.e., which proteins are most related to the disease autism, which DNAs play important role to the development of autism, which cell types have the correlation to autism and which cell components participate the process to autism. The work can be visited by the address http://134.175.110.97/bioinfo/index.jsp.

Keywords

References

  1. A. Nowak-Brzezinska and A. Wakulicz-Deja, "Exploration of rule-based knowledge bases: a knowledge engineer's support," Information Sciences, vol. 485, pp. 301-318, 2019. https://doi.org/10.1016/j.ins.2019.02.019
  2. T. E. Huang, Q. Guo, H. Sun, C. W. Tan, and T. Hu, "A deep learning approach for power system knowledge discovery based on multitask learning," IET Generation, Transmission & Distribution, vol. 13, no. 5, pp. 733-740, 2018. https://doi.org/10.1049/iet-gtd.2018.5078
  3. Y. Sato, K. Izui, T. Yamada, and S. Nishiwaki, "Data mining based on clustering and association rule analysis for knowledge discovery in multiobjective topology optimization," Expert Systems with Applications, vol. 119, pp. 247-261, 2019. https://doi.org/10.1016/j.eswa.2018.10.047
  4. M. Aarabi, E. Kessler, S. Madan-Khetarpal, U. Surti, D. Bellissimo, A. Rajkovic, and S. A. Yatsenko, "Autism spectrum disorder in females with ARHGEF9 alterations and a random pattern of X chromosome inactivation," European Journal Of Medical Genetics, vol. 62, no. 4, pp. 239-242, 2019. https://doi.org/10.1016/j.ejmg.2018.07.021
  5. D. Q. Nguyen and K. Verspoor, "From POS tagging to dependency parsing for biomedical event extraction," BMC Bioinformatics, vol. 20, article no. 72, 2019.
  6. F. I. Alam, J. Zhou, A. W. C. Liew, X. Jia, J. Chanussot, and Y. Gao, "Conditional random field and deep feature learning for hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 3, pp. 1612-1628, 2018. https://doi.org/10.1109/tgrs.2018.2867679
  7. S. F. Ahmad, M. A. Ansari, A. Nadeem, S. A. Bakheet, L. Y. AL-Ayadhi, and S. M. Attia, "Elevated IL-16 expression is associated with development of immune dysfunction in children with autism," Psychopharmacology, vol. 236, no. 2, pp. 831-838, 2019. https://doi.org/10.1007/s00213-018-5120-4
  8. M. Aarabi, E. Kessler, S. Madan-Khetarpal, U. Surti, D. Bellissimo, A. Rajkovic, and S. A. Yatsenko, "Autism spectrum disorder in females with ARHGEF9 alterations and a random pattern of X chromosome inactivation," European Journal of Medical Genetics, vol. 62, no. 4, pp. 239-242, 2019. https://doi.org/10.1016/j.ejmg.2018.07.021
  9. C. A. Edmonson, M. N. Ziats, and O. M. Rennert, "A non-inflammatory role for microglia in autism spectrum disorders," Frontiers in Neurology, vol. 7, article no. 9, 2016.
  10. J. W. Kim, J. Y. Hong, and S. M. Bae, "Microglia and autism Spectrum disorder: overview of current evidence and novel immunomodulatory treatment options," Clinical Psychopharmacology and Neuroscience, vol. 16, no. 3, pp. 246, 2018. https://doi.org/10.9758/cpn.2018.16.3.246