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Investigating the gene expression profiles of cells in seven embryonic stages with machine learning algorithms

机译：用机器学习算法研究七个胚胎阶段细胞基因表达谱

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摘要

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.
机译：胚胎细胞的发育涉及几个连续阶段，并且一些基因与胚胎发生有关。迄今为止，很少有研究在哺乳动物胚胎发生过程中系统地研究基因表达谱的变化。在该研究中，在七个阶段对小鼠胚胎细胞的基因表达谱进行了使用机器学习算法的计算分析。首先，通过强大的蒙特卡罗特征选择方法来分析配置文件，用于生成一个特征列表。其次，通过结合两个分类算法，在列表中应用增量特征选择：支持向量机（SVM）并重复增量修剪以产生误差减少（RIPPer）。通过SVM，我们提取了几种潜在基因生物标志物，表明胚胎细胞的阶段，并构建了一种最佳的SVM分类器，其产生了胚胎细胞的几乎完美的分类。此外，RIPPER算法访问了一些有趣的规则，表明不同阶段的不同表达式模式。

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