Pub Date : 2010-12-01DOI: 10.1109/GENSIPS.2010.5719689
Wei Zhang, Baryun Hwang, Baolin Wu, R. Kuang
In this paper, we explore several network propagation methods for gene selection from microarray gene expression datasets. The network propagation methods capture gene co-expression and differential expression with unified machine learning frameworks. Large scale experiments on five breast cancer datasets validated that the network propagation methods are capable of selecting genes that are more biologically interpretable and more consistent across multiple datasets, compared with the existing approaches.
{"title":"Network propagation models for gene selection","authors":"Wei Zhang, Baryun Hwang, Baolin Wu, R. Kuang","doi":"10.1109/GENSIPS.2010.5719689","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719689","url":null,"abstract":"In this paper, we explore several network propagation methods for gene selection from microarray gene expression datasets. The network propagation methods capture gene co-expression and differential expression with unified machine learning frameworks. Large scale experiments on five breast cancer datasets validated that the network propagation methods are capable of selecting genes that are more biologically interpretable and more consistent across multiple datasets, compared with the existing approaches.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125507195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-11-10DOI: 10.1109/GENSIPS.2010.5719684
Herman M. J. Sontrop, W. Verhaegh, R. Ham, M. Reinders, P. Moerland
We investigate the potential to enhance breast cancer event predictors by exploiting subtype information. We do this with a two-stage approach that first determines a sample's subtype using a recent module-driven approach, and secondly constructs a subtype-specific predictor to predict a metastasis event within five years. Our methodology is validated on a large compendium of microarray breast cancer datasets, including 43 replicate array pairs for assessing subtyping stability. Note that stratifying by subtype strongly reduces the training set sizes available to construct the individual predictors, which may decrease performance. Besides sample size, other factors like unequal class distributions and differences in the number of samples per subtype, easily obscure a fair comparison between subtype-specific predictors constructed on different subtypes, but also between subtype specific and subtype a-specific predictors. Therefore, we constructed a completely balanced experimental design, in which none of the above factors play a role and show that subtype-specific event predictors clearly outperform predictors that do not take subtype information into account.
{"title":"Subtype specific breast cancer event prediction","authors":"Herman M. J. Sontrop, W. Verhaegh, R. Ham, M. Reinders, P. Moerland","doi":"10.1109/GENSIPS.2010.5719684","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719684","url":null,"abstract":"We investigate the potential to enhance breast cancer event predictors by exploiting subtype information. We do this with a two-stage approach that first determines a sample's subtype using a recent module-driven approach, and secondly constructs a subtype-specific predictor to predict a metastasis event within five years. Our methodology is validated on a large compendium of microarray breast cancer datasets, including 43 replicate array pairs for assessing subtyping stability. Note that stratifying by subtype strongly reduces the training set sizes available to construct the individual predictors, which may decrease performance. Besides sample size, other factors like unequal class distributions and differences in the number of samples per subtype, easily obscure a fair comparison between subtype-specific predictors constructed on different subtypes, but also between subtype specific and subtype a-specific predictors. Therefore, we constructed a completely balanced experimental design, in which none of the above factors play a role and show that subtype-specific event predictors clearly outperform predictors that do not take subtype information into account.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122096057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-11-01DOI: 10.1109/GENSIPS.2010.5719683
Manohar Shamaiah, Sang Hyun Lee, H. Vikalo
We present an application of message-passing techniques to gene regulatory network inference. The network inference is posed as a constrained linear regression problem, and solved by a distributed computationally efficient message-passing algorithm. Performance of the proposed algorithm is tested on gold standard data sets and evaluated using metrics provided by the DREAM2 challenge [1]. Performance of the proposed algorithm is comparable to that of the techniques which yielded the best results in the DREAM2 challenge competition.
{"title":"Inference of gene-regulatory networks using message-passing algorithms","authors":"Manohar Shamaiah, Sang Hyun Lee, H. Vikalo","doi":"10.1109/GENSIPS.2010.5719683","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719683","url":null,"abstract":"We present an application of message-passing techniques to gene regulatory network inference. The network inference is posed as a constrained linear regression problem, and solved by a distributed computationally efficient message-passing algorithm. Performance of the proposed algorithm is tested on gold standard data sets and evaluated using metrics provided by the DREAM2 challenge [1]. Performance of the proposed algorithm is comparable to that of the techniques which yielded the best results in the DREAM2 challenge competition.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129553699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-11-01DOI: 10.1109/GENSIPS.2010.5719667
Majid I. Alsagabi, A. Tewfik
Clustering algorithms break down when the data points fall in huge-dimensional spaces. To tackle this problem, many subspace clustering methods were proposed to build up a subspace where data points cluster efficiently. The bottom-up approach is used widely to select a set of candidate features, and then to use a portion of this set to build up the hidden subspace step by step. The complexity depends exponentially or cubically on the number of the selected features. In this paper, we present SEGCLU, a SEGregation-based subspace CLUstering method which significantly reduces the size of the candidate features' set and has a cubic complexity. This algorithm was applied at noise-free data of DNA copy numbers of two groups of autistic and typically developing children to extract a potential bio-marker for autism. 85% of the individuals were classified correctly in a 13-dimensional subspace.
{"title":"Segregation-based subspace clustering for huge dimensional data","authors":"Majid I. Alsagabi, A. Tewfik","doi":"10.1109/GENSIPS.2010.5719667","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719667","url":null,"abstract":"Clustering algorithms break down when the data points fall in huge-dimensional spaces. To tackle this problem, many subspace clustering methods were proposed to build up a subspace where data points cluster efficiently. The bottom-up approach is used widely to select a set of candidate features, and then to use a portion of this set to build up the hidden subspace step by step. The complexity depends exponentially or cubically on the number of the selected features. In this paper, we present SEGCLU, a SEGregation-based subspace CLUstering method which significantly reduces the size of the candidate features' set and has a cubic complexity. This algorithm was applied at noise-free data of DNA copy numbers of two groups of autistic and typically developing children to extract a potential bio-marker for autism. 85% of the individuals were classified correctly in a 13-dimensional subspace.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130053891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-11-01DOI: 10.1109/GENSIPS.2010.5719669
F. Ay, G. Gülsoy, Tamer Kahveci
Identifying steady states that characterize the long term outcome of regulatory networks is crucial in understanding important biological processes such as cellular differentiation. Finding all possible steady states of regulatory networks is a computationally intensive task as it suffers from state space explosion problem. Here, we propose a method for finding steady states of large-scale Boolean regulatory networks. Our method exploits scale-freeness and weak connectivity of regulatory networks in order to speed up the steady state search through partitioning. In the trivial case where network has more than one component such that the components are disconnected from each other, steady states of each component are independent of those of the remaining components. When the size of at least one connected component of the network is still prohibitively large, further partitioning is necessary. In this case, we identify weakly dependent components (i.e., two components that have a small number of regulations from one to the other) and calculate the steady states of each such component independently. We then combine these steady states by taking into account the regulations connecting them. We show that this approach is much more efficient than calculating the steady states of the whole network at once when the number of edges connecting them is small. Since regulatory networks often have small in-degrees, this partitioning strategy can be used effectively in order to find their steady states. Our experimental results on real datasets demonstrate that our method leverages steady state identification to very large regulatory networks.
{"title":"Finding steady states of large scale regulatory networks through partitioning","authors":"F. Ay, G. Gülsoy, Tamer Kahveci","doi":"10.1109/GENSIPS.2010.5719669","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719669","url":null,"abstract":"Identifying steady states that characterize the long term outcome of regulatory networks is crucial in understanding important biological processes such as cellular differentiation. Finding all possible steady states of regulatory networks is a computationally intensive task as it suffers from state space explosion problem. Here, we propose a method for finding steady states of large-scale Boolean regulatory networks. Our method exploits scale-freeness and weak connectivity of regulatory networks in order to speed up the steady state search through partitioning. In the trivial case where network has more than one component such that the components are disconnected from each other, steady states of each component are independent of those of the remaining components. When the size of at least one connected component of the network is still prohibitively large, further partitioning is necessary. In this case, we identify weakly dependent components (i.e., two components that have a small number of regulations from one to the other) and calculate the steady states of each such component independently. We then combine these steady states by taking into account the regulations connecting them. We show that this approach is much more efficient than calculating the steady states of the whole network at once when the number of edges connecting them is small. Since regulatory networks often have small in-degrees, this partitioning strategy can be used effectively in order to find their steady states. Our experimental results on real datasets demonstrate that our method leverages steady state identification to very large regulatory networks.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133880389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-11-01DOI: 10.1109/GENSIPS.2010.5719672
N. Bouaynaya, R. Shterenberg, D. Schonfeld
We formulate the control problem in gene regulatory networks as an inverse perturbation problem, which provides the feasible set of perturbations that force the network to transition from an undesirable steady-state distribution to a desirable one. We derive a general characterization of such perturbations in an appropriate basis representation. We subsequently consider the optimal perturbation, which minimizes the overall energy of change between the original and controlled (perturbed) networks. The “energy” of change is characterized by the Euclidean-norm of the perturbation matrix. We cast the optimal control problem as a semi-definite programming (SDP) problem, thus providing a globally optimal solution which can be efficiently computed using standard SDP solvers. We apply the proposed control to the Human melanoma gene regulatory network and show that the steady-state probability mass is shifted from the undesirable high metastatic states to the chosen steady-state probability mass.
{"title":"Optimal perturbation control of gene regulatory networks","authors":"N. Bouaynaya, R. Shterenberg, D. Schonfeld","doi":"10.1109/GENSIPS.2010.5719672","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719672","url":null,"abstract":"We formulate the control problem in gene regulatory networks as an inverse perturbation problem, which provides the feasible set of perturbations that force the network to transition from an undesirable steady-state distribution to a desirable one. We derive a general characterization of such perturbations in an appropriate basis representation. We subsequently consider the optimal perturbation, which minimizes the overall energy of change between the original and controlled (perturbed) networks. The “energy” of change is characterized by the Euclidean-norm of the perturbation matrix. We cast the optimal control problem as a semi-definite programming (SDP) problem, thus providing a globally optimal solution which can be efficiently computed using standard SDP solvers. We apply the proposed control to the Human melanoma gene regulatory network and show that the steady-state probability mass is shifted from the undesirable high metastatic states to the chosen steady-state probability mass.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134320980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-11-01DOI: 10.1109/GENSIPS.2010.5719677
W. G. Jenkinson, J. Goutsias
Estimating the rate constants of a biochemical reaction model of cellular function is an important, albeit computationally intensive, problem in systems biology. In this paper, a variance-based sensitivity analysis approach is proposed, which can be used, as a pre-screening step, to identify parameters in a biochemical reaction system that do not appreciably influence the cost of estimation and, therefore, whose values cannot be precisely determined by parameter estimation. By only estimating the remaining parameters, appreciable qualitative and quantitative improvements can be achieved. A subset of a well-known biochemical reaction model of the EGF/ERK signaling pathway is used to illustrate the benefits achieved by the proposed method.
{"title":"A screening method for dimensionality reduction in biochemical reaction system calibration","authors":"W. G. Jenkinson, J. Goutsias","doi":"10.1109/GENSIPS.2010.5719677","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719677","url":null,"abstract":"Estimating the rate constants of a biochemical reaction model of cellular function is an important, albeit computationally intensive, problem in systems biology. In this paper, a variance-based sensitivity analysis approach is proposed, which can be used, as a pre-screening step, to identify parameters in a biochemical reaction system that do not appreciably influence the cost of estimation and, therefore, whose values cannot be precisely determined by parameter estimation. By only estimating the remaining parameters, appreciable qualitative and quantitative improvements can be achieved. A subset of a well-known biochemical reaction model of the EGF/ERK signaling pathway is used to illustrate the benefits achieved by the proposed method.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125864505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-11-01DOI: 10.1109/GENSIPS.2010.5719676
Mu-Fen Hsieh, S. Sze
With the increased availability of genome-scale data, it becomes possible to study functional relationships of genes across multiple biological networks. While most previous approaches for studying conservation of patterns in networks are through the application of network alignment algorithms or the identification of network motifs, we show that it is possible to exhaustively enumerate all graphlet alignments, which consist of subgraphs from each network that share a common topology and contain homologous proteins at the same position in the topology. We show that our algorithm is able to cover significantly more proteins than previous network alignment algorithms while achieving comparable specificity and higher sensitivity with respect to functional enrichment.
{"title":"Graphlet alignment in protein interaction networks","authors":"Mu-Fen Hsieh, S. Sze","doi":"10.1109/GENSIPS.2010.5719676","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719676","url":null,"abstract":"With the increased availability of genome-scale data, it becomes possible to study functional relationships of genes across multiple biological networks. While most previous approaches for studying conservation of patterns in networks are through the application of network alignment algorithms or the identification of network motifs, we show that it is possible to exhaustively enumerate all graphlet alignments, which consist of subgraphs from each network that share a common topology and contain homologous proteins at the same position in the topology. We show that our algorithm is able to cover significantly more proteins than previous network alignment algorithms while achieving comparable specificity and higher sensitivity with respect to functional enrichment.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128831043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-11-01DOI: 10.1109/GENSIPS.2010.5719674
Lori A. Dalton, E. Dougherty
Small sample classifier design has become a major issue in the biological and medical communities, owing to the recent development of high-throughput genomic and proteomic technologies. And as the problem of estimating classifier error is already handicapped by limited available information, it is further compounded by the necessity of reusing training-data for error estimation. Due to the difficulty of error estimation, all currently popular techniques have been heuristically devised, rather than rigorously designed based on statistical inference and optimization. However, a recently proposed error estimator has placed the problem into an optimal mean-square error (MSE) signal estimation framework in the presence of uncertainty. This results in a Bayesian approach to error estimation based on a parameterized family of feature-label distributions. These Bayesian error estimators are optimal when averaged over a given family of distributions, unbiased when averaged over a given family and all samples, and analytically address a trade-off between robustness (modeling assumptions) and accuracy (minimum mean-square error). Closed form solutions have been provided for two important examples: the discrete classification problem and linear classification of Gaussian distributions. Here we discuss the Bayesian minimum mean-square error (MMSE) error estimator and demonstrate performance on real biological data under Gaussian modeling assumptions.
{"title":"Bayesian MMSE estimation of classification error and performance on real genomic data","authors":"Lori A. Dalton, E. Dougherty","doi":"10.1109/GENSIPS.2010.5719674","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719674","url":null,"abstract":"Small sample classifier design has become a major issue in the biological and medical communities, owing to the recent development of high-throughput genomic and proteomic technologies. And as the problem of estimating classifier error is already handicapped by limited available information, it is further compounded by the necessity of reusing training-data for error estimation. Due to the difficulty of error estimation, all currently popular techniques have been heuristically devised, rather than rigorously designed based on statistical inference and optimization. However, a recently proposed error estimator has placed the problem into an optimal mean-square error (MSE) signal estimation framework in the presence of uncertainty. This results in a Bayesian approach to error estimation based on a parameterized family of feature-label distributions. These Bayesian error estimators are optimal when averaged over a given family of distributions, unbiased when averaged over a given family and all samples, and analytically address a trade-off between robustness (modeling assumptions) and accuracy (minimum mean-square error). Closed form solutions have been provided for two important examples: the discrete classification problem and linear classification of Gaussian distributions. Here we discuss the Bayesian minimum mean-square error (MMSE) error estimator and demonstrate performance on real biological data under Gaussian modeling assumptions.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114853772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-11-01DOI: 10.1109/GENSIPS.2010.5719686
Zhouyi Xu, Xiaodong Cai
The weighted stochastic simulation algorithm (wSSA) recently developed by Kuwahara and Mura and the refined wSSA proposed by Gillespie et al. based on the importance sampling technique open the door for efficient estimation of the probability of rare events in biochemical reaction systems. However, both the wSSA and the refined wSSA do not provide a systematic method for selecting the values of importance sampling parameters but require some initial guessing for those values. In this paper, we develop a systematic method for selecting the values of importance sampling parameters for the wSSA. Numerical results demonstrate that our parameter selection method can substantially improve the performance of the wSSA in terms of simulation efficiency and accuracy.
{"title":"Importance sampling method for efficient estimation of the probability of rare events in biochemical reaction systems","authors":"Zhouyi Xu, Xiaodong Cai","doi":"10.1109/GENSIPS.2010.5719686","DOIUrl":"https://doi.org/10.1109/GENSIPS.2010.5719686","url":null,"abstract":"The weighted stochastic simulation algorithm (wSSA) recently developed by Kuwahara and Mura and the refined wSSA proposed by Gillespie et al. based on the importance sampling technique open the door for efficient estimation of the probability of rare events in biochemical reaction systems. However, both the wSSA and the refined wSSA do not provide a systematic method for selecting the values of importance sampling parameters but require some initial guessing for those values. In this paper, we develop a systematic method for selecting the values of importance sampling parameters for the wSSA. Numerical results demonstrate that our parameter selection method can substantially improve the performance of the wSSA in terms of simulation efficiency and accuracy.","PeriodicalId":388703,"journal":{"name":"2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"69 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114046694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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