The Experts below are selected from a list of 589125 Experts worldwide ranked by ideXlab platform
Edward R Dougherty - One of the best experts on this subject based on the ideXlab platform.
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learning restricted boolean network model by Time Series Data
Eurasip Journal on Bioinformatics and Systems Biology, 2014Co-Authors: Hongjia Ouyang, Jie Fang, Liangzhong Shen, Edward R DoughertyAbstract:Restricted Boolean networks are simplified Boolean networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean network from Time-Series Data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean networks may be inferred from Time-Series Data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic networks and a well-studied budding yeast cell cycle network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance μ e , the normalized Hamming distance of state transition μ st , and the steady-state distribution distance μ ssd . Results show that the proposed algorithm outperforms the others according to both μ e and μ st , whereas its performance according to μ ssd is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate
Ronaldo Fumio Hashimoto - One of the best experts on this subject based on the ideXlab platform.
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Constraint-based analysis of gene interactions using restricted boolean networks and Time-Series Data.
BMC proceedings, 2011Co-Authors: Carlos H. A. Higa, Vitor H. P. Louzada, Tales P Andrade, Ronaldo Fumio HashimotoAbstract:A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and Time-Series Data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. We applied the proposed algorithm in two Data sets. First, we used an artificial Dataset obtained from a model for the budding yeast cell cycle. The second Data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from Time-Series Data of gene expression, and this inference process can be aided by a priori knowledge available.
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Constraint-based analysis of gene interactions using restricted boolean networks and Time-Series Data
BMC Proceedings, 2011Co-Authors: Carlos H. A. Higa, Vitor H. P. Louzada, Tales P Andrade, Ronaldo Fumio HashimotoAbstract:Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and Time-Series Data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two Data sets. First, we used an artificial Dataset obtained from a model for the budding yeast cell cycle. The second Data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from Time-Series Data of gene expression, and this inference process can be aided by a priori knowledge available.
Enrique Lopez Droguett - One of the best experts on this subject based on the ideXlab platform.
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failure and reliability prediction by support vector machines regression of Time Series Data
Reliability Engineering & System Safety, 2011Co-Authors: Marcio Das Chagas Moura, Isis Didier Lins, Enrique Lopez DroguettAbstract:Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting Time-to-failure and reliability of engineered components based on Time Series Data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.
Carlos H. A. Higa - One of the best experts on this subject based on the ideXlab platform.
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Constraint-based analysis of gene interactions using restricted boolean networks and Time-Series Data.
BMC proceedings, 2011Co-Authors: Carlos H. A. Higa, Vitor H. P. Louzada, Tales P Andrade, Ronaldo Fumio HashimotoAbstract:A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and Time-Series Data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. We applied the proposed algorithm in two Data sets. First, we used an artificial Dataset obtained from a model for the budding yeast cell cycle. The second Data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from Time-Series Data of gene expression, and this inference process can be aided by a priori knowledge available.
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Constraint-based analysis of gene interactions using restricted boolean networks and Time-Series Data
BMC Proceedings, 2011Co-Authors: Carlos H. A. Higa, Vitor H. P. Louzada, Tales P Andrade, Ronaldo Fumio HashimotoAbstract:Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and Time-Series Data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two Data sets. First, we used an artificial Dataset obtained from a model for the budding yeast cell cycle. The second Data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from Time-Series Data of gene expression, and this inference process can be aided by a priori knowledge available.
Seunghye J Wilson - One of the best experts on this subject based on the ideXlab platform.
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Data representation for Time Series Data mining Time domain approaches
Wiley Interdisciplinary Reviews: Computational Statistics, 2017Co-Authors: Seunghye J WilsonAbstract:In most Time Series Data mining, alternate forms of Data representation or Data preprocessing is required because of the unique characteristics of Time Series, such as high dimension the number of Data points, presence of random noise, and nonlinear relationship of the Data elements. Therefore, any Data representation method aims to achieve substantial Data reduction to a manageable size, while preserving important characteristics of the original Data, and robustness to random noise. Moreover, appropriate choice of a Data representation method may result in meaningful Data mining. Many high level representation methods of Time Series Data are based on Time domain approaches. These methods preprocess the original Data in the Time domain directly and are useful to understand the behavior of Data over Time. Piecewise approximation, Data representation by identification important points, and symbolic representation are some of the main ideas of Time domain approaches, and widely used in various fields. WIREs Comput Stat 2017, 9:e1392. doi: 10.1002/wics.1392
