Essential proteins play an important role in disease diagnosis and drug development.Many methods have been devoted to the essential protein prediction by using some kinds of biological information.However,they either ...Essential proteins play an important role in disease diagnosis and drug development.Many methods have been devoted to the essential protein prediction by using some kinds of biological information.However,they either ignore the noise presented in the biological information itself or the noise generated during feature extraction.To overcome these problems,in this paper,we propose a novel method for predicting essential proteins called attention gate-graph attention network and temporal convolutional network(AG-GATCN).In AG-GATCN method,we use improved temporal convolutional network(TCN)to extract features from gene expression sequence.To address the noise in the gene expression sequence itself and the noise generated after the dilated causal convolution,we introduce attention mechanism and gating mechanism in TCN.In addition,we use graph attention network(GAT)to extract protein–protein interaction(PPI)network features,in which we construct the feature matrix by introducing node2vec technique and 7 centrality metrics,and to solve the GAT oversmoothing problem,we introduce gated tanh unit(GTU)in GAT.Finally,two types of features are integrated by us to predict essential proteins.Compared with the existing methods for predicting essential proteins,the experimental results show that AG-GATCN achieves better performance.展开更多
In this paper, by the twist-crossing number of knots, we give an upper bound on the Euler characteristic of a kind of essential surfaces in the complements of alternating knots and almost alternating knots, which impr...In this paper, by the twist-crossing number of knots, we give an upper bound on the Euler characteristic of a kind of essential surfaces in the complements of alternating knots and almost alternating knots, which improves the estimation of the Euler characteristic of the essential surfaces with boundaries under certain conditions. Furthermore, we give the genus of the essential surfaces.展开更多
Learning Bayesian network structure is one of the most important branches in Bayesian network. The most popular graphical representative of a Bayesian network structure is an essential graph. This paper shows a combin...Learning Bayesian network structure is one of the most important branches in Bayesian network. The most popular graphical representative of a Bayesian network structure is an essential graph. This paper shows a combined algorithm according to the three rules for finding the essential graph of a given directed acyclic graph. Moreover, the complexity and advantages of this combined algorithm over others are also discussed. The aim of this paper is to present the proof of the correctness of the combined algorithm.展开更多
基金the National Natural Science Foundation of China(Grant Nos.11861045,11361033,and 62162040)。
文摘Essential proteins play an important role in disease diagnosis and drug development.Many methods have been devoted to the essential protein prediction by using some kinds of biological information.However,they either ignore the noise presented in the biological information itself or the noise generated during feature extraction.To overcome these problems,in this paper,we propose a novel method for predicting essential proteins called attention gate-graph attention network and temporal convolutional network(AG-GATCN).In AG-GATCN method,we use improved temporal convolutional network(TCN)to extract features from gene expression sequence.To address the noise in the gene expression sequence itself and the noise generated after the dilated causal convolution,we introduce attention mechanism and gating mechanism in TCN.In addition,we use graph attention network(GAT)to extract protein–protein interaction(PPI)network features,in which we construct the feature matrix by introducing node2vec technique and 7 centrality metrics,and to solve the GAT oversmoothing problem,we introduce gated tanh unit(GTU)in GAT.Finally,two types of features are integrated by us to predict essential proteins.Compared with the existing methods for predicting essential proteins,the experimental results show that AG-GATCN achieves better performance.
基金The NSF (11071106) of Chinathe Program (LR2011031) for Liaoning Excellent Talents in University
文摘In this paper, by the twist-crossing number of knots, we give an upper bound on the Euler characteristic of a kind of essential surfaces in the complements of alternating knots and almost alternating knots, which improves the estimation of the Euler characteristic of the essential surfaces with boundaries under certain conditions. Furthermore, we give the genus of the essential surfaces.
基金Supported by the National Natural Science Foundation of China (No. 60974082)
文摘Learning Bayesian network structure is one of the most important branches in Bayesian network. The most popular graphical representative of a Bayesian network structure is an essential graph. This paper shows a combined algorithm according to the three rules for finding the essential graph of a given directed acyclic graph. Moreover, the complexity and advantages of this combined algorithm over others are also discussed. The aim of this paper is to present the proof of the correctness of the combined algorithm.
