Design of biological gene information collection system based on data mining technology

Yuanjun Wu; Xiaotong Mu; Danial Kahrizi

doi:10.14715/cmb/2020.66.7.15

Issue

Vol. 66 No. 7: Issue 7

Issue Published : November 9, 2020

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Design of biological gene information collection system based on data mining technology

https://doi.org/10.14715/cmb/2020.66.7.15

Yuanjun Wu

School of Information Yungui, Anhui Finance & Trade Vocational College, Hefei230601, China

Xiaotong Mu

Management and Finance, University of Sussex, BrightonBN19RH, UK

Danial Kahrizi

Faculty of Agricultural Science and Engineering, Razi University, Kermanshah, Iran

Corresponding Author(s) : Yuanjun Wu

yuqinghou1988@yahoo.com

Cellular and Molecular Biology, Vol. 66 No. 7: Issue 7
Article Published : October 31, 2020

Abstract

At present, bioinformatics research focuses on the development from the accumulation of biological data to the integration and processing of biological data. This paper designs a bio gene information collection system based on data mining technology. In the system, the information of gene web analysis database, data mining model database and gene chip database is transferred to gene algorithm tool library, which can extract, transform and load the biological gene information, transfer the collected and processed biological gene information to gene general chip and web database analysis logic, and pass it to gene expression spectrum chip/data mining module through API function GUI, through the data mining module GUI feedback to the system users. The system hardware stores the biochip information in the virtual chip set model through the gene expression spectrum data analysis model uses the gene expression similarity analysis model to analyze the expression similarity of the biological gene information, and stores the information in the gene chip database; through the multi-layer structure model, constructs the web genome biochip including the application layer, the data processing layer and the representation layer. The information analysis module analyzes the biological gene information and stores the information in the gene web analysis database. The system software adopts the method of automatic collection of biological gene data based on the web to realize the collection of biological gene information, and gives the main implementation technology of the system. The experimental results show that the system can effectively collect biological gene information, and has high accuracy and anti-noise performance.

Keywords

Data Mining Biological Gene Information Collection Gene Expression Profile Web Analysis Virtual Chipset

Wu, Y., Mu, X., & Kahrizi, D. (2020). Design of biological gene information collection system based on data mining technology. Cellular and Molecular Biology, 66(7), 93–102. https://doi.org/10.14715/cmb/2020.66.7.15

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References

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References

Ju YS, Tae MS, Dong CS. Data mining of web-based documents on social networking sites that included suicide-related words among Korean adolescents. J Adolesc Health 2016; 59: 668-673.

Thomas C, Elisabeth C, Francesco I. GDSCtools for mining pharmacogenomic interactions in cancer. Bioinformatics 2017; 34: 1226-1228.

You XW, Yao T, Xing QZ. NOSEP: nonoverlapping sequence pattern mining with gap constraints. IEEE Trans Cybern 2017; 48(10): 2809-2822.

Zheng WH, Mao ZL, Christos C. Schema theory-based data engineering in gene expression programming for big data analytics. IEEE Trans Evol Comput 2017. 22(5):792-804.

Victor WC, Raymond KW, Chi HC.The design of a cloud-based tracker platform based on system-of-systems service architecture. Inf Syst Front 2017; 19: 1-17.

Wang L, Chen Q, Gao H. Framework of fault trace for smart substation based on big data mining technology. Autom Electric Power Syst 2018; 42: 84-91.

Mei YD. The application of data warehouse model based on the integration of emerging technologies in business data mining. J Comput Theor Nanosci 2016; 13(12): 9581-9585.

Lázaro B, René C, Raudel H. On the design of hardware-software architectures for frequent itemsets mining on data streams. J Intell Inf Syst 2017; 50: 1-26.

Mohamed I, Afdel K, Mustapha B. Distributed intrusion detection system for cloud environments based on data mining techniques. Procedia Computer Sci 2018; 127: 35-41.

Hong BX, Hai XW, Ming ZQ. In silico drug repositioning for the treatment of Alzheimer's disease using molecular docking and gene expression data. RSC Adv 2016; 6: 98080-98090.

Hai XX, Fei YY, Sheng H. Erratum: Bayes performance of batch data mining based on functional dependencies. Int J Pattern Recog Artif Intell 2019; 33(03): 1959011.

Bin Y, Qiran W, Xue MW. On extraction of cancer informative genes and gene expression data mining. J Bionanosci 2016; 10: 293-299(7).

Bao, HX, Ting, L. Physical health data mining of college students based on DRF algorithm. Wirel Pers Commun 2018; 102: 1-11.

Lu ZX, Li JN, Li X. Research on classification and recognition method of ionospheric phase contamination. J China Acad Electron Inform Technol 2016; 11: 503-509.

Yuan H, Ding XP, Wang BR. Research on effect on dc-dc converter caused by parasitic resistance. J Power Supply 2016; 14: 86-94.

Yao ZM, Pan F, Yu XH. Energy-saving and emission-reduction display platform design of PV power plant based on ZigBee wireless acquisition. Chin J Power Sources 2016; 40: 1993-1996.

Luo NH, Tao JY. Liu JQ. Research on heterogeneous monitoring between business systems based on big data outlier mining algorithm. Autom Instrumen 2019; 179-182.

Tian BH, Yi L. Wang XW. Construction of phage display of antigenic variability gene fragments library of bird flu virus. J Jilin Univ (Science Edition): 2019; 989-996.

Wu HL, Ren XY. Specific data mining algorithm based on fuzzy constraint database. Comput Simul 2016; 33: 240-243.

Talat F, Wang K. Comparative Bioinformatics Analysis of the Chloroplast Genomes of a Wild Diploid Gossypium and Two Cultivated Allotetraploid Species. Iran J Biotechnol 2015; 13(3): 47-56.

Son J, Jeong H, Lee E, No S, Park D, Chung H. Identification of specific gene expression after exposure to low dose ionizing radiation revealed through integrative analysis of cDNA microarray data and the interactome. Int J Radiat Res 2019; 17 (1) :15-23.

Kuai H, Zhong N. The Extensible Data-Brain Model: Architecture, Applications and Directions. J Comput Sci 2020: 101103.

Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. J Big Data 2019; 6(1):54.

Author biographies is not available.