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首页> 外文期刊>International Journal on Informatics Visualization: JOIV >Feature Selection Techniques for Selecting Proteins that Influence Mouse Down Syndrome Using Genetic Algorithms and Random Forests
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Feature Selection Techniques for Selecting Proteins that Influence Mouse Down Syndrome Using Genetic Algorithms and Random Forests

机译：用于使用遗传算法和随机林选择影响小鼠鼠标综合征的蛋白质的特征选择技术

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Feature selection technique is a technique to reduce data dimensions which are widely used to find the set of features that best represent data. One area of science that often applies this technique is bioinformatics. An example of its application is the selection of significant proteins in the case of Down syndrome. To find out the most influential protein, experiments were carried out on normal mice with trisomy rats (down syndrome mice) totaling 1080 sample and obtained 77 levels of protein expression. The analysis carried out was divided into three groups. Each group was searched for the most influential proteins using genetic algorithms with fitness calculations using random forest algorithms. The results of the protein selection of the three data groups indicate the relationship of the selected proteins to the improvement of learning ability and memory. The results of evaluating selected protein models show a high degree of accuracy, which is above 98.7% for each data group.
机译：特征选择技术是一种降低数据维度的技术，这些技术被广泛用于查找最佳代表数据的一组功能。通常适用这种技术的一个科学领域是生物信息学。其应用的一个例子是在唐氏综合征的情况下选择重要的蛋白质。为了找出最有影响力的蛋白质，在具有三胞​​大鼠（唐氏综合小鼠）的正常小鼠上进行实验，总共1080只样品，得到77级蛋白质表达。进行的分析分为三组。使用随机林算法的遗传算法搜索每组最有影响力的蛋白质。三个数据组的蛋白质选择的结果表明所选蛋白质的关系改善学习能力和记忆。评估所选蛋白质模型的结果显示出高度的精度，每个数据组高于98.7％。

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