Gaussian network model(GNM)is an efficient method to investigate the structural dynamics of biomolecules.However,the application of GNM on RNAs is not as good as that on proteins,and there is still room to improve the...Gaussian network model(GNM)is an efficient method to investigate the structural dynamics of biomolecules.However,the application of GNM on RNAs is not as good as that on proteins,and there is still room to improve the model.In this study,two novel approaches,named the weighted GNM(wGNM)and the force-constant-decayed GNM(fcdGNM),were proposed to enhance the performance of ENM in investigating the structural dynamics of RNAs.In wGNM,the force constant for each spring is weighted by the number of interacting heavy atom pairs between two nucleotides.In fcdGNM,all the pairwise nucleotides were connected by springs and the force constant decayed exponentially with the separate distance of the nucleotide pairs.The performance of these two proposed models was evaluated by using a non-redundant RNA structure database composed of 51 RNA molecules.The calculation results show that both the proposed models outperform the conventional GNM in reproducing the experimental B-factors of RNA structures.Compared with the conventional GNM,the Pearson correlation coefficient between the predicted and experimental B-factors was improved by 9.85%and 6.76%for wGNM and fcdGNM,respectively.Our studies provide two candidate methods for better revealing the dynamical properties encoded in RNA structures.展开更多
For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-d...For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.展开更多
文摘Gaussian network model(GNM)is an efficient method to investigate the structural dynamics of biomolecules.However,the application of GNM on RNAs is not as good as that on proteins,and there is still room to improve the model.In this study,two novel approaches,named the weighted GNM(wGNM)and the force-constant-decayed GNM(fcdGNM),were proposed to enhance the performance of ENM in investigating the structural dynamics of RNAs.In wGNM,the force constant for each spring is weighted by the number of interacting heavy atom pairs between two nucleotides.In fcdGNM,all the pairwise nucleotides were connected by springs and the force constant decayed exponentially with the separate distance of the nucleotide pairs.The performance of these two proposed models was evaluated by using a non-redundant RNA structure database composed of 51 RNA molecules.The calculation results show that both the proposed models outperform the conventional GNM in reproducing the experimental B-factors of RNA structures.Compared with the conventional GNM,the Pearson correlation coefficient between the predicted and experimental B-factors was improved by 9.85%and 6.76%for wGNM and fcdGNM,respectively.Our studies provide two candidate methods for better revealing the dynamical properties encoded in RNA structures.
基金supported by the National Natural Science Foundation of China(61202473)the Fundamental Research Funds for Central Universities(JUSRP111A49)+1 种基金"111 Project"(B12018)the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.
