School of Life Sciences, Shanghai University, Shanghai 200444, China; College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China; Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, China.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China.
Genomics. 2020 May;112(3):2524-2534. doi: 10.1016/j.ygeno.2020.02.004. Epub 2020 Feb 8.
The development of embryonic cells involves several continuous stages, and some genes are related to embryogenesis. To date, few studies have systematically investigated changes in gene expression profiles during mammalian embryogenesis. In this study, a computational analysis using machine learning algorithms was performed on the gene expression profiles of mouse embryonic cells at seven stages. First, the profiles were analyzed through a powerful Monte Carlo feature selection method for the generation of a feature list. Second, increment feature selection was applied on the list by incorporating two classification algorithms: support vector machine (SVM) and repeated incremental pruning to produce error reduction (RIPPER). Through SVM, we extracted several latent gene biomarkers, indicating the stages of embryonic cells, and constructed an optimal SVM classifier that produced a nearly perfect classification of embryonic cells. Furthermore, some interesting rules were accessed by the RIPPER algorithm, suggesting different expression patterns for different stages.
胚胎细胞的发育涉及几个连续的阶段，其中一些基因与胚胎发生有关。迄今为止，很少有研究系统地研究哺乳动物胚胎发生过程中基因表达谱的变化。在这项研究中，我们使用机器学习算法对来自 7 个阶段的小鼠胚胎细胞的基因表达谱进行了计算分析。首先，通过强大的蒙特卡罗特征选择方法对谱进行分析，以生成特征列表。其次，通过结合两种分类算法：支持向量机（SVM）和重复增量剪枝以产生错误减少（RIPPER），在列表上应用增量特征选择。通过 SVM，我们提取了几个潜在的基因生物标志物，指示胚胎细胞的阶段，并构建了一个最佳的 SVM 分类器，几乎可以完美地对胚胎细胞进行分类。此外，RIPPER 算法还获取了一些有趣的规则，表明不同阶段的表达模式不同。
