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Vol. 66 No. 7: Issue 7

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Design of abnormal data detection system for protein gene library based on data mining technology

https://doi.org/10.14715/cmb/2020.66.7.16
Cuixia Liu
Information Department, Shijiazhuang Vocational College of Finance & Economics, Shijiazhuang 050061, China
Yuwei Wang
Global Media Communication, The University of Melbourne, Melbourne 3006, Australia

Corresponding Author(s) : Cuixia Liu

yichuanli1963@yahoo.com

Cellular and Molecular Biology, Vol. 66 No. 7: Issue 7

Abstract

In view of the shortcomings of the current abnormal data detection system of the protein gene library, such as low detection rate and high error detection rate, the abnormal data detection system of the protein gene library based on data mining technology is designed. The protein gene enters the firewall module of the system, and enters the immune module when it does not match the firewall rules; the memory detector in the immune module presents the protein gene, if the memory detector does not match the protein gene, the mature detector presents the protein gene, if the mature detector does not match the protein gene, it is determined as the normal protein gene data package, if it matches, it is considered that The abnormal data of protein gene was processed by the collaborative stimulation module, and the control module controlled by C8051F060 chip to detect the abnormal data of protein gene library. The immune module generates new protein gene sequences through an immature detector, simulates the immune mechanism of protein gene through a mature detector module, and simulates the secondary response in the abnormal data detection system of protein gene library through memory detector. The system introduces data mining technology into the detection and uses a two-level dynamic optimization algorithm to calculate the ASG similarity value of protein gene secondary structure arrangement. According to this value, the abnormal data detection of the protein gene library is realized by randomly generating protein genes, negative selection, clone selection and copying memory cells through gene expression. The experimental results show that the system can quickly detect abnormal data of the protein gene library, ensure the detection efficiency, and the detection accuracy reaches 97.1%. The system can reduce the error rate of normal protein gene detection as an abnormal protein gene.

Keywords

Data Mining Technology Protein Gene Library Abnormal Data Detector Accuracy Detection System
Liu, C., & Wang, Y. (2020). Design of abnormal data detection system for protein gene library based on data mining technology. Cellular and Molecular Biology, 66(7), 103–110. https://doi.org/10.14715/cmb/2020.66.7.16
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