针对目前蛋白质基因库异常数据检测系统存在的不足,例如低检测率和高错误检测率,设计了基于数据挖掘技术的蛋白质基因库异常数据检测系统。蛋白质基因进入系统的防火墙模块,并在不符合防火墙规则时进入免疫模块;免疫模块中的内存检测器呈现蛋白质基因,如果记忆检测器与蛋白质基因不匹配,成熟的探测器呈现蛋白质基因,如果成熟的检测器与蛋白质基因不匹配,它被确定为正常的蛋白质基因数据包,如果匹配的话,认为协同刺激模块对蛋白质基因异常数据进行了处理,C8051F060芯片控制控制模块检测蛋白质基因库的异常数据。免疫模块通过未成熟的检测器产生新的蛋白质基因序列,通过成熟的检测模块模拟蛋白质基因的免疫机制,并通过记忆检测器模拟蛋白质基因库异常数据检测系统中的二次响应。该系统将数据挖掘技术引入到检测中,采用两级动态优化算法计算蛋白质基因二级结构排列的ASG相似度值。根据这个值,通过随机生成蛋白质基因来实现蛋白质基因库的异常数据检测,否定选择,克隆选择和复制记忆细胞通过基因表达。实验结果表明,该系统能够快速检测蛋白质基因库的异常数据,确保检测效率,检测精度达到97.1%。该系统可以降低正常蛋白质基因作为异常蛋白质基因检测的错误率。
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.