The Experts below are selected from a list of 80733 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
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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 Dougherty, Wenbin LiuAbstract: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 , the normalized Hamming distance of state transition , and the steady-state distribution distance μssd. Results show that the proposed Algorithm outperforms the others according to both and , whereas its performance according to μssd is intermediate between best-fit and the three-Rule Algorithms. Thus, our new Algorithm is more appropriate for inferring interactions between genes from time-series data.
Wenbin Liu - 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 Dougherty, Wenbin LiuAbstract: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 , the normalized Hamming distance of state transition , and the steady-state distribution distance μssd. Results show that the proposed Algorithm outperforms the others according to both and , whereas its performance according to μssd is intermediate between best-fit and the three-Rule Algorithms. Thus, our new Algorithm is more appropriate for inferring interactions between genes from time-series data.
Hongjia Ouyang - 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
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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 Dougherty, Wenbin LiuAbstract: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 , the normalized Hamming distance of state transition , and the steady-state distribution distance μssd. Results show that the proposed Algorithm outperforms the others according to both and , whereas its performance according to μssd is intermediate between best-fit and the three-Rule Algorithms. Thus, our new Algorithm is more appropriate for inferring interactions between genes from time-series data.
Sasitharan Balasubramaniam - One of the best experts on this subject based on the ideXlab platform.
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analysis and pattern deduction on linguistic numeric based mean and fuzzy association Rule Algorithm on any geo referenced crime point data integrated with google map
Soft Computing for Problem Solving, 2012Co-Authors: R. Sridhar, S R Sathyraj, Sasitharan BalasubramaniamAbstract:Data mining is receiving more attention to find the underlying patterns in crime data. It is need to act quickly to reduce crime activity and find out the links between various available data sources. The government are continuing to call upon modern geographic information systems to find the more intensive area of crime in order to protect their communities and assets. Real time solutions can provide significant resources and push the capability of law enforcement closer to the pulse of criminal activity.
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Analysis and Pattern Deduction on Linguistic based Mean and Fuzzy Association Rule Algorithm on any Geo-referenced Crime Point Data
Data mining and knowledge engineering, 2011Co-Authors: R. Sridhar, S.r. Sathyaraj, Sasitharan BalasubramaniamAbstract:Data mining is receiving more attention to find the underlying patterns in crime data. It is need to act quickly to reduce crime activity and find out the links between various available data sources. The government is continuing to call upon modern geographic information systems to find the more intensive area of crime in order to protect their communities and assets. Real time solutions can provide significant resources and push the capability of law enforcement closer to the pulse of criminal activity. There are 3 Algorithms to study the pattern of any point data and for better inferences and interpretation. In this study, Mean Algorithm using Linguistic variable finds the most occurred crime at particular location among different types of crime. Fuzzy associations Rule Algorithm on point data formulate the Rules among the crimes is a novel means for knowledge discovery in the crime domain, supported by experimental results using Mapobject and VB. Mean Algorithm using crime find the location not shown by earlier Algorithm where sensitivity of crime is high.
Liangzhong Shen - 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
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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 Dougherty, Wenbin LiuAbstract: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 , the normalized Hamming distance of state transition , and the steady-state distribution distance μssd. Results show that the proposed Algorithm outperforms the others according to both and , whereas its performance according to μssd is intermediate between best-fit and the three-Rule Algorithms. Thus, our new Algorithm is more appropriate for inferring interactions between genes from time-series data.
