Tumors have drawn increasing attention recently because of their heterogeneous interior structures. Particularly, single-cell RNA (scRNA) mechanics have made important contributions to the field of tumor research. To investigate the cell types and identify similar types of gene markers present inside a tumor, machine learning classifier, optimization, and neural network models were applied to scRNA sequencing data. Indeed, even though single-cell analysis is a more powerful tool, several issues have been identified, such as transcriptional noise that alters gene expression and degrades mRNA. Recently, optimization models for single-cell analysis have been developed to address these kinds of issues, and encouraging results have been reported. scRNA sequencing is popular because it produces biological information in the form of patterns that are displayed within the transcriptome profile. The neural network approach plays an important role in understanding and identifying these distinct patterns. A single layer perceptron was introduced to better analyze the data pattern within gene expression profiles. Finally, recently developed optimization models with machine learning classifiers are compared with the proposed single layer perceptron. The single layer perceptron performs better compared with other models such as extra tree classifier with genetic algorithm, k-nearest neighbors with bat optimization, decision tree with gray wolf optimization, random forest with firefly optimization, and Gaussian naïve Bayes with artificial bee colony optimization. This study also focused on classifying these unique cell types and gene markers using scRNA sequence datasets. The proposed single layer perceptron was assessed using two datasets: normal mucosa and colorectal tumors. Our findings showed that the proposed single layer perceptron performed exceptionally well with accuracy, precision, recall, and F1 value.

肿瘤由于其异质的内部结构最近引起了越来越多的关注。特别是单细胞RNA（scRNA）机制为肿瘤研究领域做出了重要贡献。为了研究细胞类型并识别肿瘤内存在的相似类型的基因标记，机器学习分类器、优化和神经网络模型被应用于 scRNA 测序数据。事实上，尽管单细胞分析是一种更强大的工具，但已经发现了几个问题，例如改变基因表达和降解 mRNA 的转录噪音。最近，开发了单细胞分析的优化模型来解决此类问题，并报告了令人鼓舞的结果。 scRNA 测序很受欢迎，因为它以转录组图谱中显示的模式形式产生生物信息。神经网络方法在理解和识别这些不同模式方面发挥着重要作用。引入单层感知器是为了更好地分析基因表达谱中的数据模式。最后，将最近开发的带有机器学习分类器的优化模型与所提出的单层感知器进行比较。与其他模型相比，例如使用遗传算法的额外树分类器、使用蝙蝠优化的 k-近邻、使用灰狼优化的决策树、使用萤火虫优化的随机森林以及使用人工蜂群优化的高斯朴素贝叶斯，单层感知器的性能更好。这项研究还重点关注使用 scRNA 序列数据集对这些独特的细胞类型和基因标记进行分类。使用两个数据集评估所提出的单层感知器：正常粘膜和结直肠肿瘤。我们的研究结果表明，所提出的单层感知器在准确度、精确度、召回率和 F1 值方面表现得非常好。