The discovery of novel cancer genes is one of the main goals in cancer research.Bioinformatics methods can be used to accelerate cancer gene discovery,which may help in the understanding of cancer and the development ...The discovery of novel cancer genes is one of the main goals in cancer research.Bioinformatics methods can be used to accelerate cancer gene discovery,which may help in the understanding of cancer and the development of drug targets.In this paper,we describe a classifier to predict potential cancer genes that we have developed by integrating multiple biological evidence,including protein-protein interaction network properties,and sequence and functional features.We detected 55 features that were significantly different between cancer genes and non-cancer genes.Fourteen cancer-associated features were chosen to train the classifier.Four machine learning methods,logistic regression,support vector machines(SVMs),BayesNet and decision tree,were explored in the classifier models to distinguish cancer genes from non-cancer genes.The prediction power of the different models was evaluated by 5-fold cross-validation.The area under the receiver operating characteristic curve for logistic regression,SVM,Baysnet and J48 tree models was 0.834,0.740,0.800 and 0.782,respectively.Finally,the logistic regression classifier with multiple biological features was applied to the genes in the Entrez database,and 1976 cancer gene candidates were identified.We found that the integrated prediction model performed much better than the models based on the individual biological evidence,and the network and functional features had stronger powers than the sequence features in predicting cancer genes.展开更多
Sn/carbon-fibers（CFs） nanocomposite has been prepared by chemical vapor deposition with in-situ catalytic growth of CFs.The nanocomposite has been characterized by X-ray diffraction（XRD）,field emission scanning el...Sn/carbon-fibers（CFs） nanocomposite has been prepared by chemical vapor deposition with in-situ catalytic growth of CFs.The nanocomposite has been characterized by X-ray diffraction（XRD）,field emission scanning electron microscopy（FE-SEM）,transmission electron microscopy（TEM） and Raman spectrum.The electrochemical performance of the nanocomposite has been investigated by galvanostatic cycling and cyclic voltammetry（CV）.It has been found that a three-dimensional conductive network forms by the interconnected CFs,which offers conductive channels for the Sn nanoparticles.The nanocomposite gives a first charge capacity of 385 mAh.g-1 and exhibits an improved cycling stability than bare Sn.展开更多
Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neur...Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.展开更多
In this paper, the volumetric properties of pure and mixture of ionic liquids are predicted using the developed statistical mechanical equation of state in different temperatures, pressures and mole fractions. The tem...In this paper, the volumetric properties of pure and mixture of ionic liquids are predicted using the developed statistical mechanical equation of state in different temperatures, pressures and mole fractions. The temperature dependent parameters of the equation of state have been calculated using corresponding state correlation based on only the density at 298.15 K as scaling constants. The obtained mean of deviations of modified equation of state for density of all pure ionic liquids for 1662 data points was 0.25%. In addition, the performance of the artificial neural network(ANN) with principle component analysis(PCA) based on back propagation training with28 neurons in hidden layer for predicting of behavior of binary mixtures of ionic liquids was investigated. The AADs of a collection of 568 data points for all binary systems using the EOS and the ANN at various temperatures and mole fractions are 1.03% and 0.68%, respectively. Moreover, the excess molar volume of all binary mixtures is predicted using obtained densities of EOS and ANN, and the results show that these properties have good agreement with literature.展开更多
According to different forms of synchronized region, complex networks are divided into type Ⅰ （unbounded synchronization region） and type Ⅱ （bounded synchronization region） networks. This paper presents a rewiri...According to different forms of synchronized region, complex networks are divided into type Ⅰ （unbounded synchronization region） and type Ⅱ （bounded synchronization region） networks. This paper presents a rewiring algorithm to enhance the synchronizability of type Ⅰ and type Ⅱ networks. By utilizing the algorithm for an unweighted and undirected network, a better synchronizability of network with the same number of nodes and edges can be obtained. Numerical simulations on several different network models are used to support the proposed procedure. The relationship between different topological properties of the networks and the number of rewirings are shown. It finds that the final optimized network is independent of the initial network, and becomes homogeneous. In addition the optimized networks have similar structural properties in the sense of degree, and node and edge betweenness centralities. However, they do not have similar cluster coefficients for type Ⅱ networks. The research may be useful for designing more synchronizable networks and understanding the synchronization behaviour of networks.展开更多
Objective Using graph theory analysis,this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties bet...Objective Using graph theory analysis,this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties between bipolar depression(BD)patients with and without obsessive-compulsive symptoms(OCS).Methods A total of 90 BD patients(27 males,63 females;median age 19.0(22.0,25.0)years)were recruited from the psychiatric outpatient and inpatient departments of the First Affiliated Hospital of Jinan University between March 2018 and December 2022.Fifty healthy controls(19 males,31 females;median age:23.0(20.0,27.0)years)were also enrolled.The BD patients were divided into two groups based on the presence of OCS:53 with OCS(OCS group)and 37 without OCS(NOCS group).Resting-state structural and functional MRI data were collected for all participants to construct gray matter structural and functional networks.Graph therory analysis was aapplied to calculate network topological metrics such as small-world properties.The structural and functional network topological properties were compared among the BD-OCS,BD-nOCS,and control groups.Partial correlation analysis was conducted to examine the association between network topological metrics with significant group differences and Yale-Brown Obsessive-Compulsive Scale(Y-BOCS)scores.Support vector machines(SVM)were used with these metrics as classificationfeaturevalues toimproveediagnostic accuracy through pairwise group classification.Results Structural network analysis of gray matter:compared to HC group,both OCS group and NOCS group showed increasedshortesttpathlengthand standardized characteristic path length(shortest path length:0.78 and 0.80 vs.0.69;normalized characteristic path length:0.48 and 0.49 vs.0.43),and decreased global efficiency(0.21 and 0.21 vs.0.24)compared to the HC group(permutation test,all P<0.05).Compared to NOCS and HC groups,the OCS group showed increased nodal centrality and betweenness centrality in the right rolandic operculum and left superior occipital gyrus(permutation test,all P<0.05).Functional network analysis of gray matter:compared to the NOCS group,the OCS group showed increased node efficiency and decreased betweenness centrality in the cerebellum(t=2.15,-3.04;all P<0.05);compared to HC groups,the OCS group showed decreased betweenness centrality in the cerebellum and left inferior frontal gyrus,along with increased node centrality and nodal efficiency in the right transverse temporal gyrus(t=-2.99,-3.61,3.06,3.10;all P<0.05).In the 0CS group,betweenness centrality in the left inferior frontal gyrus positively correlated with Y-BOCS scale obsessive thinking score(r=0.303,P=0.034).Nodal centrality and node efficiency of the right transverse temporal gyrus negatively correlated with Y-BOCS total score(r=-0.301,-0.311)and Y-BOCS obsessional thinking scores(r=-0.385,-0.380)separately(all P<0.05).SVM classification:the combined network features achieved an area under the curve of 0.80 in distinguising OCS from NOCS patients.Conclusion BDOCS and BD-nOCS patients both exhibit consistent changes in gray matter structural network topology,with theOCSSgroup displaying more pronounced nodal topological abnormalities.Multi-network feature integration demostrates potential for diagnostic classfication.展开更多
A new metal-organic framework（MOF） based on metal clusters as secondary building units（SBU）,has been synthesized and structurally characterized.The reported MOF presents an interesting 8-connected self-penetrating...A new metal-organic framework（MOF） based on metal clusters as secondary building units（SBU）,has been synthesized and structurally characterized.The reported MOF presents an interesting 8-connected self-penetrating coordination network based on dinuclear cadmium cluster with a 4^（24）·5·6~3 topology. Moreover,the thermal stability and luminescence property of this compound have been investigated.展开更多
Link prediction is an important task that estimates the probability of there being a link between two disconnected nodes. The similarity-based algorithm is a very popular method that employs the node similarities to f...Link prediction is an important task that estimates the probability of there being a link between two disconnected nodes. The similarity-based algorithm is a very popular method that employs the node similarities to find links. Most of these types of algorithms focus only on the contribution of common neighborhoods between two nodes. In sociological theory relationships within three degrees are the strong ties that can trigger social behaviors.Thus, strong ties can provide more connection opportunities for unconnected nodes in the networks. As critical topological properties in networks, nodes degrees and node clustering coefficients are well-suited for describing the tightness of connections between nodes. In this paper, we characterize node similarity by utilizing the strong ties of the ego network（i.e., paths within three degrees） and its close connections（node degrees and node clustering coefficients）. We propose a link prediction algorithm that combines topological properties with strong ties, which we called the TPSR algorithm. This algorithm includes TPSR2, TPSR3, and the TPSR4 indices. We evaluate the performance of the proposed algorithm using the metrics of precision and the Area Under the Curve（AUC）. Our experimental results show the TPSR algorithm to perform remarkably better than others.展开更多
基金supported by the National Natural Science Foundation of China (31000591,31000587,31171266)
文摘The discovery of novel cancer genes is one of the main goals in cancer research.Bioinformatics methods can be used to accelerate cancer gene discovery,which may help in the understanding of cancer and the development of drug targets.In this paper,we describe a classifier to predict potential cancer genes that we have developed by integrating multiple biological evidence,including protein-protein interaction network properties,and sequence and functional features.We detected 55 features that were significantly different between cancer genes and non-cancer genes.Fourteen cancer-associated features were chosen to train the classifier.Four machine learning methods,logistic regression,support vector machines(SVMs),BayesNet and decision tree,were explored in the classifier models to distinguish cancer genes from non-cancer genes.The prediction power of the different models was evaluated by 5-fold cross-validation.The area under the receiver operating characteristic curve for logistic regression,SVM,Baysnet and J48 tree models was 0.834,0.740,0.800 and 0.782,respectively.Finally,the logistic regression classifier with multiple biological features was applied to the genes in the Entrez database,and 1976 cancer gene candidates were identified.We found that the integrated prediction model performed much better than the models based on the individual biological evidence,and the network and functional features had stronger powers than the sequence features in predicting cancer genes.
基金supported by Zijin Program of Zhejiang University,Chinathe Fundamental Research Funds for the Central Universities (No. 2010QNA4003)+1 种基金the Ph.D.Programs Foundation of Ministry of Education of China(No. 20100101120024)the Foundation of Education Office of Zhejiang Province (No. Y201016484)
文摘Sn/carbon-fibers（CFs） nanocomposite has been prepared by chemical vapor deposition with in-situ catalytic growth of CFs.The nanocomposite has been characterized by X-ray diffraction（XRD）,field emission scanning electron microscopy（FE-SEM）,transmission electron microscopy（TEM） and Raman spectrum.The electrochemical performance of the nanocomposite has been investigated by galvanostatic cycling and cyclic voltammetry（CV）.It has been found that a three-dimensional conductive network forms by the interconnected CFs,which offers conductive channels for the Sn nanoparticles.The nanocomposite gives a first charge capacity of 385 mAh.g-1 and exhibits an improved cycling stability than bare Sn.
文摘Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.
文摘In this paper, the volumetric properties of pure and mixture of ionic liquids are predicted using the developed statistical mechanical equation of state in different temperatures, pressures and mole fractions. The temperature dependent parameters of the equation of state have been calculated using corresponding state correlation based on only the density at 298.15 K as scaling constants. The obtained mean of deviations of modified equation of state for density of all pure ionic liquids for 1662 data points was 0.25%. In addition, the performance of the artificial neural network(ANN) with principle component analysis(PCA) based on back propagation training with28 neurons in hidden layer for predicting of behavior of binary mixtures of ionic liquids was investigated. The AADs of a collection of 568 data points for all binary systems using the EOS and the ANN at various temperatures and mole fractions are 1.03% and 0.68%, respectively. Moreover, the excess molar volume of all binary mixtures is predicted using obtained densities of EOS and ANN, and the results show that these properties have good agreement with literature.
基金Project supported by the Science Foundation of the Education Bureau of Liaoning Province of China(Grant No.2008497)
文摘According to different forms of synchronized region, complex networks are divided into type Ⅰ （unbounded synchronization region） and type Ⅱ （bounded synchronization region） networks. This paper presents a rewiring algorithm to enhance the synchronizability of type Ⅰ and type Ⅱ networks. By utilizing the algorithm for an unweighted and undirected network, a better synchronizability of network with the same number of nodes and edges can be obtained. Numerical simulations on several different network models are used to support the proposed procedure. The relationship between different topological properties of the networks and the number of rewirings are shown. It finds that the final optimized network is independent of the initial network, and becomes homogeneous. In addition the optimized networks have similar structural properties in the sense of degree, and node and edge betweenness centralities. However, they do not have similar cluster coefficients for type Ⅱ networks. The research may be useful for designing more synchronizable networks and understanding the synchronization behaviour of networks.
文摘Objective Using graph theory analysis,this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties between bipolar depression(BD)patients with and without obsessive-compulsive symptoms(OCS).Methods A total of 90 BD patients(27 males,63 females;median age 19.0(22.0,25.0)years)were recruited from the psychiatric outpatient and inpatient departments of the First Affiliated Hospital of Jinan University between March 2018 and December 2022.Fifty healthy controls(19 males,31 females;median age:23.0(20.0,27.0)years)were also enrolled.The BD patients were divided into two groups based on the presence of OCS:53 with OCS(OCS group)and 37 without OCS(NOCS group).Resting-state structural and functional MRI data were collected for all participants to construct gray matter structural and functional networks.Graph therory analysis was aapplied to calculate network topological metrics such as small-world properties.The structural and functional network topological properties were compared among the BD-OCS,BD-nOCS,and control groups.Partial correlation analysis was conducted to examine the association between network topological metrics with significant group differences and Yale-Brown Obsessive-Compulsive Scale(Y-BOCS)scores.Support vector machines(SVM)were used with these metrics as classificationfeaturevalues toimproveediagnostic accuracy through pairwise group classification.Results Structural network analysis of gray matter:compared to HC group,both OCS group and NOCS group showed increasedshortesttpathlengthand standardized characteristic path length(shortest path length:0.78 and 0.80 vs.0.69;normalized characteristic path length:0.48 and 0.49 vs.0.43),and decreased global efficiency(0.21 and 0.21 vs.0.24)compared to the HC group(permutation test,all P<0.05).Compared to NOCS and HC groups,the OCS group showed increased nodal centrality and betweenness centrality in the right rolandic operculum and left superior occipital gyrus(permutation test,all P<0.05).Functional network analysis of gray matter:compared to the NOCS group,the OCS group showed increased node efficiency and decreased betweenness centrality in the cerebellum(t=2.15,-3.04;all P<0.05);compared to HC groups,the OCS group showed decreased betweenness centrality in the cerebellum and left inferior frontal gyrus,along with increased node centrality and nodal efficiency in the right transverse temporal gyrus(t=-2.99,-3.61,3.06,3.10;all P<0.05).In the 0CS group,betweenness centrality in the left inferior frontal gyrus positively correlated with Y-BOCS scale obsessive thinking score(r=0.303,P=0.034).Nodal centrality and node efficiency of the right transverse temporal gyrus negatively correlated with Y-BOCS total score(r=-0.301,-0.311)and Y-BOCS obsessional thinking scores(r=-0.385,-0.380)separately(all P<0.05).SVM classification:the combined network features achieved an area under the curve of 0.80 in distinguising OCS from NOCS patients.Conclusion BDOCS and BD-nOCS patients both exhibit consistent changes in gray matter structural network topology,with theOCSSgroup displaying more pronounced nodal topological abnormalities.Multi-network feature integration demostrates potential for diagnostic classfication.
基金supported by the National Science Foundation of China(No.51073079)the Natural Science Fund of Tianjin,China (No.10JCZDJC22100)the Fundamental Research Funds for the Central Universities
文摘A new metal-organic framework（MOF） based on metal clusters as secondary building units（SBU）,has been synthesized and structurally characterized.The reported MOF presents an interesting 8-connected self-penetrating coordination network based on dinuclear cadmium cluster with a 4^（24）·5·6~3 topology. Moreover,the thermal stability and luminescence property of this compound have been investigated.
基金partially supported by the National Natural Science Foundation of China(Nos.61673020,61402006,and 61702003)the National High-Tech Research and Development(863)Program of China(No.2015AA124102)+1 种基金Humanities and Social Science Research on Youth Fund Project,Ministry of Education(No.14YJC860020)Anhui Provincial Natural Science Foundation(No.1708085MF160)
文摘Link prediction is an important task that estimates the probability of there being a link between two disconnected nodes. The similarity-based algorithm is a very popular method that employs the node similarities to find links. Most of these types of algorithms focus only on the contribution of common neighborhoods between two nodes. In sociological theory relationships within three degrees are the strong ties that can trigger social behaviors.Thus, strong ties can provide more connection opportunities for unconnected nodes in the networks. As critical topological properties in networks, nodes degrees and node clustering coefficients are well-suited for describing the tightness of connections between nodes. In this paper, we characterize node similarity by utilizing the strong ties of the ego network（i.e., paths within three degrees） and its close connections（node degrees and node clustering coefficients）. We propose a link prediction algorithm that combines topological properties with strong ties, which we called the TPSR algorithm. This algorithm includes TPSR2, TPSR3, and the TPSR4 indices. We evaluate the performance of the proposed algorithm using the metrics of precision and the Area Under the Curve（AUC）. Our experimental results show the TPSR algorithm to perform remarkably better than others.
