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BioMed Research International
Volume 2018, Article ID 6807059, 7 pages
https://doi.org/10.1155/2018/6807059
Research Article

Detecting Early Warning Signal of Influenza A Disease Using Sample-Specific Dynamical Network Biomarkers

School of Science, Jiangnan University, Wuxi 214122, China

Correspondence should be addressed to Jie Gao; nc.ude.nangnaij@eijoag

Received 5 September 2017; Revised 30 November 2017; Accepted 25 December 2017; Published 31 January 2018

Academic Editor: Yudong Cai

Copyright © 2018 Shanshan Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. W. Joost Lesterhuis, A. Bosco, M. J. Millward, M. Small, A. K. Nowak, and R. A. Lake, “Dynamic versus static biomarkers in cancer immune checkpoint blockade: Unravelling complexity,” Nature Reviews Drug Discovery, vol. 16, no. 4, pp. 264–272, 2017. View at Publisher · View at Google Scholar · View at Scopus
  2. M. A. Dahlem, J. Kurths, M. D. Ferrari, K. Aihara, M. Scheffer, and A. May, “Understanding migraine using dynamic network biomarkers,” Cephalalgia, vol. 35, no. 7, pp. 627–630, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Nikolaou, P. A. Gutiérrez, A. Durán, I. Dicaire, F. Fernández-Navarro, and C. Hervás-Martínez, “Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm,” Climate Dynamics, vol. 44, no. 7-8, pp. 1919–1933, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. D. Karahoca, A. Karahoca, and Ö. Yavuz, “An early warning system approach for the identification of currency crises with data mining techniques,” Neural Computing and Applications, vol. 23, no. 7-8, pp. 2471–2479, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Liu, M. Li, Z.-P. Liu, J. Wu, L. Chen, and K. Aihara, “Identifying critical transitions and their leading biomolecular networks in complex diseases,” Scientific Reports, vol. 2, article no. 813, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. P. Chen, R. Liu, Y. Li, and L. Chen, “Detecting critical state before phase transition of complex biological systems by hidden Markov model,” Bioinformatics, vol. 32, no. 14, pp. 2143–2150, 2016. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Achiron, I. Grotto, R. Balicer, D. Magalashvili, A. Feldman, and M. Gurevich, “Microarray analysis identifies altered regulation of nuclear receptor family members in the pre-disease state of multiple sclerosis,” Neurobiology of Disease, vol. 38, no. 2, pp. 201–209, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. J. S. Kinney, T. Morelli, T. Braun et al., “Saliva/pathogen biomarker signatures and periodontal disease progression,” Journal of Dental Research, vol. 90, no. 6, pp. 752–758, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Jin, X. Zhou, H. Wang et al., “The knowledge-integrated network biomarkers discovery for major adverse cardiac events,” Journal of Proteome Research, vol. 7, no. 9, pp. 4013–4021, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Chen, R. Liu, Z.-P. Liu, M. Li, and K. Aihara, “Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers,” Scientific Reports, vol. 2, article 342, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Gao, L. Zhang, and P. Jin, “Influenza pandemic early warning research on HA/NA protein sequences,” Current Bioinformatics, vol. 9, no. 3, pp. 228–233, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. T. Zeng, S.-Y. Sun, Y. Wang, H. Zhu, and L. Chen, “Network biomarkers reveal dysfunctional gene regulations during disease progression,” FEBS Journal, vol. 280, no. 22, pp. 5682–5695, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Liu, X. Wang, K. Aihara, and L. Chen, “Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers,” Medicinal Research Reviews, vol. 34, pp. 455–478, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Li, T. Zeng, R. Liu, and L. Chen, “Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: Study of type 2 diabetes by cross-tissue analysis,” Briefings in Bioinformatics, vol. 15, no. 2, pp. 229–243, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. A. D. Torshizi and L. Petzold, “Sparse Pathway-Induced Dynamic Network Biomarker Discovery for Early Warning Signal Detection in Complex Diseases,” IEEE/ACM Transactions on Computational Biology & Bioinformatics, pp. 1–8, 2017. View at Google Scholar
  16. X. Liu, Y. Wang, H. Ji, K. Aihara, and L. Chen, “Personalized characterization of diseases using sample-specific networks,” Nucleic Acids Research, vol. 44, no. 22, article no. e164, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. X. Liu, X. Chang, R. Liu, X. Yu, L. Chen, and K. Aihara, “Quantifying critical states of complex diseases using single-sample dynamic network biomarkers,” PLoS Computational Biology, vol. 13, no. 7, Article ID e1005633, 2017. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Liu, X. Yu, X. Liu, D. Xu, K. Aihara, and L. Chen, “Identifying critical transitions of complex diseases based on a single sample,” Bioinformatics, vol. 30, no. 11, pp. 1579–1586, 2014. View at Publisher · View at Google Scholar
  19. S. D. Shapira, I. Gat-Viks, B. O. V. Shum et al., “A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection,” Cell, vol. 139, no. 7, pp. 1255–1267, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. B. T. Sherman, D. W. Huang, Q. Tan et al., “DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis,” BMC Bioinformatics, vol. 8, article 426, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Zhang, N. Tang, Y. Liu, and E.-H. Wang, “ARVCF expression is significantly correlated with the malignant phenotype of non-small cell lung cancer,” Molecular Carcinogenesis, vol. 54, no. 1, pp. E185–E191, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. S.-F. Hsu, W.-C. Su, K.-S. Jeng, and M. M. C. Lai, “A host susceptibility gene, DR1, facilitates influenza a virus replication by suppressing host innate immunity and enhancing viral RNA replication,” Journal of Virology, vol. 89, no. 7, pp. 3671–3682, 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Antonopoulou, F. Baziaka, T. Tsaganos et al., “Role of tumor necrosis factor gene single nucleotide polymorphisms in the natural course of 2009 influenza A H1N1 virus infection,” International Journal of Infectious Diseases, vol. 16, no. 3, pp. e204–e208, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. W. Liu, L. Jiang, C. Bian et al., “Role of CX3CL1 in Diseases,” Archivum Immunologiae et Therapia Experimentalis, vol. 64, no. 5, pp. 371–383, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. K. K. W. To, J. Zhou, Y.-Q. Song et al., “Surfactant protein B gene polymorphism is associated with severe influenza,” CHEST, vol. 145, no. 6, pp. 1237–1243, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. Z.-P. Liu, “Identifying network-based biomarkers of complex diseases from high-throughput data,” Biomarkers in Medicine, vol. 10, no. 6, pp. 633–650, 2016. View at Publisher · View at Google Scholar · View at Scopus