【佳学基因检测】随机森林与人工神经网络联合诊断多囊卵巢综合征模型的建立与分析

病理基因检测合理性

学习基因检测结果分析中的基因解码方法听到《Biomed Res Int》在. 2020 Aug 20;2020:2613091.发表了一篇题目为《随机森林与人工神经网络联合诊断多囊卵巢综合征模型的建立与分析》肿瘤靶向药物治疗基因检测临床研究文章。该研究由Ning-Ning Xie, Fang-Fang Wang, Jue Zhou, Chang Liu, Fan Qu等完成。促进了多囊卵巢综合征的精准治疗与个性化用药的发展，进一步强调了基因信息检测与分析多囊卵巢综合征的重要性。

多囊卵巢综合征精准治疗临床研究内容关键词：

多囊卵巢综合征, PCOS,代谢性疾病,生殖疾病,人工神经网络,基因检测

多囊卵巢综合征基因检测临床应用结果

多囊卵巢综合征 (PCOS) 是最常见的代谢和生殖内分泌疾病之一。然而，很少有研究试图开发基于基因生物标志物的诊断模型。在这项研究中，基因解码基因检测应用了一种计算基因解码基因检测的研究方法，通过结合两种机器学习算法，包括随机森林（RF）和人工神经网络（ANN），来识别基因生物标志物并构建诊断模型。基因解码基因检测从包含 76 个 PCOS 样本和 57 个正常样本的 Gene Expression Omnibus (GEO) 数据库中收集了基因表达数据；使用了五个数据集，包括一个用于筛选差异表达基因 (DEG) 的数据集、两个训练数据集和两个验证数据集。首先，基于RF，264个DEG中的12个关键基因被确定为对PCOS和正常样本的分类至关重要。此外，这些关键基因的权重分别使用具有微阵列和 RNA-seq 训练数据集的 ANN 计算。此外，开发了两种类型数据集的诊断模型并命名为neuralPCOS。最后，使用两个验证数据集按曲线下面积 (AUC) 测试和比较神经 PCOS 与其他两组标记基因的性能。基因解码基因检测的模型在微阵列数据集中实现了 0.7273 的 AUC，在 RNA-seq 数据集中实现了 0.6488。总之，基因解码基因检测发现了基因生物标志物并开发了一种新的 PCOS 诊断模型，这将有助于诊断。

肿瘤发生与复发转移国际数据库描述：

Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.