■LPS会增加牙周组织中炎症因子的产生,特别是牙龈卟啉单胞菌,对牙周组织的损害加剧。细菌LPS引起的牙周膜干细胞外泌体的再生和修复改变。miRNA由外泌体携带至受体细胞以影响表观遗传功能。因此,这项研究旨在利用深度学习算法在暴露于细菌LPS的PDLSC干细胞中发现新的micro-RNA生物标志物,以了解激活途径。
■使用NCBIGEODATASETGSE163489,发现在健康和LPS诱导的PDLSC细胞之间差异最大的差异表达的微小RNA。深度学习分析,使用随机森林,人工神经网络c,支持向量机(SVM),以及在橙色数据挖掘工具包中实现的线性回归模型,鉴定了新型microRNA生物标志物。橙色数据挖掘工具包用于microRNA表达数据的深度学习分析,为分类等机器学习任务提供用户友好的环境,回归,和聚类。
■随机森林成为优越的模型,与神经网络(R2=.952,RMSE=0.332)相比,获得最高的R2得分(.985)和最低的RMSE(0.189),线性回归(R2=.949,RMSE=0.343),和支持向量机(R2=.931,RMSE=0.398)。这表明其优越的捕获microRNA表达数据中潜在模式的能力。鉴于其强大的性能,随机森林有望识别新型生物标志物,开发更准确的诊断工具,并可能指导患者分层进行牙周病的针对性治疗干预。
■当前的研究利用对microRNA表达数据的深度学习分析来鉴定与炎症小体激活和抗凋亡途径相关的新型生物标志物。这些发现有望指导牙周疾病新型治疗策略的开发。然而,未来的研究有必要使用独立的数据集和实验方法来验证这些生物标志物.
UNASSIGNED: The production of inflammatory factors in
periodontium is increased by LPS, particularly from P. gingivalis, and the damage to periodontal tissues is exacerbated. Exosomes from periodontal ligament stem cells change regeneration and repair brought on by bacterial LPS. MiRNAs are carried by exosomes to recipient cells to affect epigenetic functions. Thus, this study aims to utilize deep learning algorithms to uncover novel micro-RNA biomarkers in bacterial LPS-exposed PDLSC stem cells to understand the activation pathway.
UNASSIGNED: Using NCBI GEO DATA SET GSE163489, the most differentially expressed micro RNAs were found to differ between healthy and LPS-induced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friendly environment for machine learning tasks like classification, regression, and clustering.
UNASSIGNED: Random Forest emerged as the superior model, achieving the highest R 2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks (R 2 = .952, RMSE = 0.332), Linear Regression (R 2 = .949, RMSE = 0.343), and SVM (R 2 = .931, RMSE = 0.398). This suggests its superior ability to capture the underlying patterns in the microRNA expression data. Given its robust performance, Random Forest holds promise for identifying novel biomarkers, developing more accurate diagnostic tools, and potentially guiding the stratification of patients for targeted therapeutic interventions in periodontal disease.
UNASSIGNED: The current study utilizes deep learning analysis of microRNA expression data to identify novel biomarkers associated with inflammasome activation and anti-apoptotic pathways. These findings hold promise for guiding the development of novel therapeutic strategies for periodontal disease. However, future studies are warranted to validate these biomarkers using independent datasets and experimental methods.