Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted conside...Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted considerable attention within the bioinformatics field.Recently,Bayesian network(BN)techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell data.However,current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells.A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony(PDABC),named PDABC.Specifically,PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric.The experimental results on several simulated datasets,as well as a previously published multi-parameter fluorescence-activated cell sorter dataset,indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.展开更多
The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from...The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.展开更多
催化裂化(fluid catalytic cracking,FCC)沉降器跑剂故障复杂,故障发生时多个参数会偏离正常状态,需迅速检测和缓解,以保障FCC装置长周期稳定运行。提出了一种集成自编码器(autoencoder,AE)和多尺度符号转移熵(multi-scale symbol trans...催化裂化(fluid catalytic cracking,FCC)沉降器跑剂故障复杂,故障发生时多个参数会偏离正常状态,需迅速检测和缓解,以保障FCC装置长周期稳定运行。提出了一种集成自编码器(autoencoder,AE)和多尺度符号转移熵(multi-scale symbol transfer entropy,MSTE)的无监督跑剂故障检测方法(AEM),充分考虑了时间序列数据中复杂的时间依赖关系及故障后时间序列的非平稳性。将双重注意力(dual attention,DA)机制融入长短期记忆单元(long short-term memory,LSTM)的编码器-解码器结构中,其中特征注意力(feature attention,FA)机制通过为输入特征变量分配权重,动态强调关键特征并识别主要故障变量;时间注意力(temporal attention,TA)机制则通过为时间维度内的每个时间步分配权重,捕获其依赖信息,从而进一步提升检测效果。此外,利用MSTE方法构建了非平稳过程的因果关系图,揭示了变量之间的时间延迟并排除了间接因果关系。通过分析沉降器快分头封头穿孔过程,验证了AEM的有效性,且提升了决策过程的可解释性。展开更多
基金National Natural Science Foundation of China,Grant/Award Numbers:62106009,62276010R&D Program of Beijing Municipal Education Commission,Grant/Award Numbers:KM202210005030,KZ202210005009。
文摘Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted considerable attention within the bioinformatics field.Recently,Bayesian network(BN)techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell data.However,current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells.A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony(PDABC),named PDABC.Specifically,PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric.The experimental results on several simulated datasets,as well as a previously published multi-parameter fluorescence-activated cell sorter dataset,indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.
基金Supported by the National Natural Science Foundation of China under Grant Nos.7110317971102129+1 种基金11121403by Program for Young Innovative Research Team in China University of Political Science and Law
文摘The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.
