BACKGROUND The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis(PBC)and finding relevant biomarkers for diagnosis and therapeutic e...BACKGROUND The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis(PBC)and finding relevant biomarkers for diagnosis and therapeutic evaluation.AIM To determine PBC-associated hub genes and assess their clinical utility for disease prediction.METHODS PBC expression data were obtained from the Gene Expression Omnibus database.Overlapping genes from differential expression analysis and weighted gene coexpression network analysis(WGCNA)were identified as key genes for PBC.Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes.Hub genes were identified in protein-protein interaction(PPI)networks using the Degree algorithm in Cytoscape software.The relationship between hub genes and immune cells was investigated.Finally,a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC.RESULTS We identified 71 overlapping key genes using differential expression analysis and WGCNA.These genes were primarily enriched in pathways related to cytokinecytokine receptor interaction,and Th1,Th2,and Th17 cell differentiation.We utilized Cytoscape software and identified five hub genes(CD247,IL10,CCL5,CCL3,and STAT3)in PPI networks.These hub genes showed a strong correlation with immune cell infiltration in PBC.However,inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk.CONCLUSION Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment,thereby offering significant clinical utility.展开更多
Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function.Though progress has been made to elucidate the process,molecular mechanisms of different c...Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function.Though progress has been made to elucidate the process,molecular mechanisms of different classes of cardiomyopathies remain elusive.This paper aims to describe the similarities and differences in molecular features of dilated cardiomyopathy(DCM)and ischemic cardiomyopathy(ICM).We firstly detected the co-expressed modules using the weighted gene co-expression network analysis(WGCNA).Significant modules associated with DCM/ICM were identified by the Pearson correlation coefficient(PCC)between the modules and the phenotype of DCM/ICM.The differentially expressed genes in the modules were selected to perform functional enrichment.The potential transcription factors(TFs)prediction was conducted for transcription regulation of hub genes.Apoptosis and cardiac conduction were perturbed in DCM and ICM,respectively.TFs demonstrated that the biomarkers and the transcription regulations in DCM and ICM were different,which helps make more accurate discrimination between them at molecular levels.In conclusion,comprehensive analyses of the molecular features may advance our understanding of DCM and ICM causes and progression.Thus,this understanding may promote the development of innovative diagnoses and treatments.展开更多
实现城市对外客运枢纽抵站客流的精准预测,是增强枢纽接续运输运力调度科学性的重要前提。为提高枢纽抵站客流的预测精度,研究了基于超参数优化的鲸鱼算法与双向长短期记忆神经网络模型(whale optimization algorithm and bi-directiona...实现城市对外客运枢纽抵站客流的精准预测,是增强枢纽接续运输运力调度科学性的重要前提。为提高枢纽抵站客流的预测精度,研究了基于超参数优化的鲸鱼算法与双向长短期记忆神经网络模型(whale optimization algorithm and bi-directional long short-term memory,WOA-Bi-LSTM)组合的客流预测方法。融合历史抵站客流数据及天气、日期、时段等多源信息,分析抵站客流的时变特性,并开展不同影响因素与枢纽抵站客流量间的相关性分析。改进了传统双向长短期记忆神经网络模型(bi-directional long short-term memory,Bi-LSTM)的参数设置方法,用鲸鱼算法(whale optimization algorithm,WOA)代替手动调参,选取学习效率(η)与隐藏神经元个数(H)2个对模型预测精度具有较大影响的超参数进行最优超参数组合搜寻,通过计算其适应度函数进行循环逻辑判断,实现参数自适应优化。通过不断寻优,获取最优参数组合值,确定设置η为0.0603、H为120,并输出预测结果和3个模型精度评价指标(R^(2)判定系数,平均绝对误差与均方根误差);同时构建了3种不同超参数优化算法改进的Bi-LSTM组合模型、2种基于WOA算法改进的其他组合模型,以及2种未改进的神经网络模型与WOA-Bi-LSTM模型使用相同的抵站客流数据集进行多维度对比,验证所建模型的优越性与鲁棒性。结果表明:WOA-Bi-LSTM模型在节假日、工作日与非工作日等不同枢纽抵站客流预测场景下均体现出良好的适用性,与其他模型相比,R2相关系数最大,达到0.9514,表示所建模型的拟合效果最好;平均绝对误差与均方根误差最小,分别为762.96与556.25,误差相较于其他模型至少减少5.6%和3.2%。展开更多
基金Supported by School-Level Key Projects at Bengbu Medical College,No.2021byzd109。
文摘BACKGROUND The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis(PBC)and finding relevant biomarkers for diagnosis and therapeutic evaluation.AIM To determine PBC-associated hub genes and assess their clinical utility for disease prediction.METHODS PBC expression data were obtained from the Gene Expression Omnibus database.Overlapping genes from differential expression analysis and weighted gene coexpression network analysis(WGCNA)were identified as key genes for PBC.Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes.Hub genes were identified in protein-protein interaction(PPI)networks using the Degree algorithm in Cytoscape software.The relationship between hub genes and immune cells was investigated.Finally,a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC.RESULTS We identified 71 overlapping key genes using differential expression analysis and WGCNA.These genes were primarily enriched in pathways related to cytokinecytokine receptor interaction,and Th1,Th2,and Th17 cell differentiation.We utilized Cytoscape software and identified five hub genes(CD247,IL10,CCL5,CCL3,and STAT3)in PPI networks.These hub genes showed a strong correlation with immune cell infiltration in PBC.However,inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk.CONCLUSION Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment,thereby offering significant clinical utility.
基金supported by the National Natural Science Foundation of China under Grants No.61720106004 and No.61872405the Key R&D Project of Sichuan Province,China under Grants No.20ZDYF2772 and No.2020YFS0243.
文摘Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function.Though progress has been made to elucidate the process,molecular mechanisms of different classes of cardiomyopathies remain elusive.This paper aims to describe the similarities and differences in molecular features of dilated cardiomyopathy(DCM)and ischemic cardiomyopathy(ICM).We firstly detected the co-expressed modules using the weighted gene co-expression network analysis(WGCNA).Significant modules associated with DCM/ICM were identified by the Pearson correlation coefficient(PCC)between the modules and the phenotype of DCM/ICM.The differentially expressed genes in the modules were selected to perform functional enrichment.The potential transcription factors(TFs)prediction was conducted for transcription regulation of hub genes.Apoptosis and cardiac conduction were perturbed in DCM and ICM,respectively.TFs demonstrated that the biomarkers and the transcription regulations in DCM and ICM were different,which helps make more accurate discrimination between them at molecular levels.In conclusion,comprehensive analyses of the molecular features may advance our understanding of DCM and ICM causes and progression.Thus,this understanding may promote the development of innovative diagnoses and treatments.
文摘实现城市对外客运枢纽抵站客流的精准预测,是增强枢纽接续运输运力调度科学性的重要前提。为提高枢纽抵站客流的预测精度,研究了基于超参数优化的鲸鱼算法与双向长短期记忆神经网络模型(whale optimization algorithm and bi-directional long short-term memory,WOA-Bi-LSTM)组合的客流预测方法。融合历史抵站客流数据及天气、日期、时段等多源信息,分析抵站客流的时变特性,并开展不同影响因素与枢纽抵站客流量间的相关性分析。改进了传统双向长短期记忆神经网络模型(bi-directional long short-term memory,Bi-LSTM)的参数设置方法,用鲸鱼算法(whale optimization algorithm,WOA)代替手动调参,选取学习效率(η)与隐藏神经元个数(H)2个对模型预测精度具有较大影响的超参数进行最优超参数组合搜寻,通过计算其适应度函数进行循环逻辑判断,实现参数自适应优化。通过不断寻优,获取最优参数组合值,确定设置η为0.0603、H为120,并输出预测结果和3个模型精度评价指标(R^(2)判定系数,平均绝对误差与均方根误差);同时构建了3种不同超参数优化算法改进的Bi-LSTM组合模型、2种基于WOA算法改进的其他组合模型,以及2种未改进的神经网络模型与WOA-Bi-LSTM模型使用相同的抵站客流数据集进行多维度对比,验证所建模型的优越性与鲁棒性。结果表明:WOA-Bi-LSTM模型在节假日、工作日与非工作日等不同枢纽抵站客流预测场景下均体现出良好的适用性,与其他模型相比,R2相关系数最大,达到0.9514,表示所建模型的拟合效果最好;平均绝对误差与均方根误差最小,分别为762.96与556.25,误差相较于其他模型至少减少5.6%和3.2%。
