Abstract
In recent years, the focus of bioinformatics research has turned to biological data processing and information extraction. New mining algorithm was designed to mine target gene fragment efficiently from a huge amount of gene data and to study specific gene expression in this paper. The extracted gene data was filtered in order to remove redundant gene data. Then the binary tree was constructed according to the Pearson correlation coefficient between gene data and processed by gSpan frequent subgraph mining algorithm. Finally, the results were visually analyzed in grayscale image way which helped us to find out the target gene. Compared with the existing target gene mining algorithms, such as integrated decision feature gene selection algorithm, our approach enjoys the advantages of higher accuracy and processing high-dimensional data. The proposed algorithm has sufficient theoretical basis, not only makes the results more efficient, but also makes the possibility of error results less. Moreover, the dimension of the data is much higher than the dimension of the data set used by the existing algorithm, so the algorithm is more practical.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Michihiro, K., George, K.: Gene classification using expression profiles: a feasibility study. Int. J. Artif. Intell. Tools 14(04), 641–660 (2001)
Lee, I., Blom, U.M., Wang, P.I., et al.: Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 21(7), 1109 (2011)
Sabau, G., Bologa, R., Bologa, R., et al.: Collaborative network for the development of an informational system in the SOA context for the university management. In: International Conference on Computer Technology and Development, pp. 307–311. IEEE (2009)
Shuman, J., Twombly, J.: Collaborative Business. In: Collaborative Networks Are The Organization: An Innovation in Organization Design and Management, 8 vols. The Rhythm of Business, Inc., Newton (2009)
Alon, U., Barkai, N., Nootterman, D.A., et al.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Science 96(12), 6745–6750 (1999)
Jie, Z., Cheng-quan, G., Jun-rong, C., Li-xin, G.: Tumor identification based on gene expression profiles and the search about extraction of the feature genes. Math. Pract. Theory 41(14), 67–79 (2011)
Ya-ning, Z., Yan-hui, Z.: Extraction of tumor gene and its classification based on SNR. J. Xiangfan Univ. 32(8), 13–16 (2011)
Quan-jin, L., Ying-xin, L., Xiao-gang, R.: Cancer information gene identification based on statistical method. J. Beijing Univ. Technol. 31(2), 122–125 (2005)
Yongxiu, C.: Understanding of correlation coefficient (7), 15–19 (2011)
Hong-bin, L., Guang-zhong, H., Qiu-ting, G.: Similarity retrieval method of organic mass spectrometry based on the Pearson correlation coefficient. Chem. Anal. Meterage 24(3), 33–37 (2015)
Niyogi, X.: Locality preserving projections. In: Neural Information Processing Systems, vol. 16, p. 153 (2004)
Yong-chao, W.: A novel D-S combination method of conflicting evidences based on pearson correlation coefficient. Telecommun. Eng. 52(4), 466–471 (2012)
Jie, L., Li-jun, D., Sheng-nan, T.: Refinement procedure for Eigen genes of colon carcinoma based on BB-SIR. World SCI-Tech R&D 33(4), 588–591 (2011)
Shoujue, W., Lingfei, Z.: Gene selection for gene expression data analysis. Micro Comput. Inf. 24(3–3), 193–194 (2008)
Jing-jing, S., Li-bo, W., Wei, L.: Gene selection for cancer diagnosis. Comput. Eng. Appl., 218–220 (2010)
Jun, W.: Method of effective DNA microarray data feature extraction. Modern Electron. Tech. 37(13), 95–98 (2014)
Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: ICDM. IEEE (2002)
Lin, T.H., Lin, C.H., Pan, T.M.: The implication of probiotics in the prevention of dental caries. Appl. Microbiol. Biotechnol. 102(2), 577–586 (2018)
Philip, N., Suneja, B., Walsh, L.J.: Ecological approaches to dental caries prevention: paradigm shift or Shibboleth? Caries Res. 52(1–2), 153–165 (2018)
Liu, H., Bebu, I., Li, X.: Microarray probes and probe sets. Front. Biosci. 2(1), 325 (2010)
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China under No. 51877144 and No. 61872219.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Lu, L., Ren, X., Qi, L., Cui, C., Jiao, Y. (2019). Target Gene Mining Algorithm Based on gSpan. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-12981-1_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12980-4
Online ISBN: 978-3-030-12981-1
eBook Packages: Computer ScienceComputer Science (R0)