数据挖掘技术在动物造模中的应用

Application of Data Mining Techniques in the Process of Animal Modeling

关键词:  数据挖掘  动物造模  SPSS MODELER

Key words:  data mining  animal modeling  SPSS MODELER

摘要:

目的 将数据挖掘技术引入动物造模的过程中，使用数据挖掘技术建立动物造模的计算机仿真模型，展示数据挖掘技术在动物造模中的价值。方法 将30只SD大鼠，♂，随机分为对照组和实验组。实验组采用完全弗氏佐剂右后足趾腱膜下注射致炎造模，对照组同部位注射等量生理盐水。第0，14，21天测量大鼠足跖部的厚度；在不同时点对关节部位进行关节炎指数(AI)的评定；第0，7，14，21天观察大鼠的体质量变化；第14天和第21天分别随机选取5只大鼠，断颈处死取血测IL-1β、IL-17、TNF-α含量并观察病理切片。将上述采集的指标纳入数据库，利用SPSS MODELER进行数据挖掘分析。结果 利用数据挖掘技术建立了3个模型：综合模型、C5.0决策树模型和神经网络模型，三者的正确率依次为95%，90%，100%，累计增益曲线均有不同程度的明显提升，同AI评分及病理切片结果吻合。结论 本研究通过结合佐剂性关节炎大鼠造模的实例，详细对数据挖掘技术在动物造模中应用的流程、方法、优化和评价进行了说明和阐述，新的数据处理方法的引入可以针对多种评估指标构建出不同的预测评估模型，有效地对数据进行解析并提取有意义的信息辅助决策。

Abstract:

OBJECTIVE To use data mining technology in the process of animal modeling to create a computer simulation model, and show the value of data mining technology. METHODS Thirty male SD rats were randomly divided into control group and model group, administered with intradermal injection of 0.25 mL normal saline and 0.25 mL complete Freund’s adjuvant, respectively, and adjust the dose to 0.1 mL four days later. Measure the joint swelling degree on 0, 14 and 21d. Assess the arthritic inde (AI) in different times and record rat weight on 0, 7, 14 and 21d. After 14 d and 21 d, five rats were randomly selected from each group, the ankle joints were prepared for histopathologic study and the level of IL-1β, IL-17 and TNF-α in serum were determined by enzyme-linked immunosorbent assay, respectively. Then the collected data was entered into the database, and SPSS MODELER for data mining analysis was used. RESULTS Three simulation models were established with data mining techniques: the integrated model, C5.0 decision tree model and neural network model, the correct rate for these models were 95%, 90%, 100%, respectively. The cumulative gain curve of these models improved significantly in varying degrees, consistent with AI score and pathological results. CONCLUSION Combined with a practical example (adjuvant arthritis rats model), we describe and elaborate the application process, method, optimization and evaluation of data mining technology in animal modeling. With the introduction of new data processing methods, we can build a serious of forecast evaluation models with different evaluation indicators, and make the right decision by using the information more effectively.