Preeclampsia (PE) is a maternal disease that causes maternal and child death. Treatment and preventive measures are not sound enough. The problem of PE screening has attracted much attention. The purpose of this study is to screen placental mRNA to obtain the best PE biomarkers for identifying patients with PE. We use Limma in the R language to screen out the 48 differentially expressed genes with the largest differences and used correlation-based feature selection algorithms to reduce the dimensionality and avoid attribute redundancy arising from too many mRNA samples participating in the classification. After reducing the mRNA attributes, the mRNA samples are sorted from large to small according to information gain. In this study, a classifier model is designed to identify whether samples had PE through mRNA in the placenta. To improve the accuracy of classification and avoid overfitting, three classifiers, including C4.5, AdaBoost, and multilayer perceptron, are used. We use the majority voting strategy integrated with the differentially expressed genes and the genes filtered by the best subset method as comparison methods to train the classifier. The results show that the classification accuracy rate has increased from 79% to 82.2%, and the number of mRNA features has decreased from 48 to 13. This study provides clues for the main PE biomarkers of mRNA in the placenta and provides ideas for the treatment and screening of PE.

中文翻译：

---

综合学习：筛选用于识别胎盘 mRNA 样本中先兆子痫的最佳生物标志物

先兆子痫（PE）是一种导致母婴死亡的孕产妇疾病。治疗和预防措施还不够健全。PE筛选问题备受关注。本研究的目的是筛选胎盘 mRNA 以获得用于识别 PE 患者的最佳 PE 生物标志物。我们使用R语言中的Limma来筛选出差异最大的48个差异表达基因，并使用基于相关性的特征选择算法来降低维数，避免因参与分类的mRNA样本过多而产生的属性冗余。减少mRNA属性后，将mRNA样本按照信息增益从大到小排序。在这项研究中，设计了一个分类器模型来通过胎盘中的 mRNA 来识别样本是否具有 PE​​。为了提高分类的准确性并避免过拟合，使用了三个分类器，包括 C4.5、AdaBoost 和多层感知器。我们使用结合差异表达基因和通过最佳子集方法过滤的基因的多数投票策略作为比较方法来训练分类器。结果表明，分类准确率从79%提高到82.2%，mRNA特征数从48个减少到13个。本研究为胎盘mRNA的主要PE生物标志物提供线索，为治疗提供思路和PE的筛选。我们使用结合差异表达基因和通过最佳子集方法过滤的基因的多数投票策略作为比较方法来训练分类器。结果表明，分类准确率从79%提高到82.2%，mRNA特征数从48个减少到13个。本研究为胎盘mRNA的主要PE生物标志物提供线索，为治疗提供思路和PE的筛选。我们使用结合差异表达基因和通过最佳子集方法过滤的基因的多数投票策略作为比较方法来训练分类器。结果表明，分类准确率从79%提高到82.2%，mRNA特征数从48个减少到13个。本研究为胎盘mRNA的主要PE生物标志物提供线索，为治疗提供思路和PE的筛选。