The Experts below are selected from a list of 12564 Experts worldwide ranked by ideXlab platform
Y Pan - One of the best experts on this subject based on the ideXlab platform.
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identifying essential proteins via integration of protein interaction and gene expression data
Bioinformatics and Biomedicine, 2012Co-Authors: Xiwei Tang, Jianxin Wang, Y PanAbstract:Essential proteins are vital for an organism's viability under a variety of conditions. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency. In this work, Pearson correlation coefficient (PCC) is used to bridge the gap between PPI and gene expression data. Based on PCC and Edge Clustering Coefficient (ECC), a new Centrality Measure, i.e., the weighted degree Centrality (WDC), is developed to achieve the reliable prediction of essential proteins. WDC is employed to identify essential proteins in the yeast PPI network in order to estimate its performance. For comparison, other prediction technologies are also performed to identify essential proteins. Some evaluation methods are used to analyze the results from various prediction approaches. The analyses prove that WDC outperforms other state-of-the-art ones. At the same time, the analyses also mean that it is an effective way to predict essential proteins by means of integrating different data sources.
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a new essential protein discovery method based on the integration of protein protein interaction and gene expression data
BMC Systems Biology, 2012Co-Authors: Hanhui Zhang, Jianxin Wang, Y PanAbstract:Background: Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins’ essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based Centrality Measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results: In this paper, we propose a new Centrality Measure, named PeC, based on the integration of proteinprotein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed Centrality Measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized a-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic Centrality Measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins. Conclusions: We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new Centrality Measure, PeC, is an effective essential protein discovery method.
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essential protein discovery based on network motif and gene ontology
Bioinformatics and Biomedicine, 2011Co-Authors: Wooyoung Kim, Jianxin Wang, Y PanAbstract:Essential proteins are indispensable to support cellular life and constitute a minimal set required for a living cell. Fast progress in high-throughput technologies and large amount of data enable to discover essential proteins in system level by analyzing protein-protein interaction networks. A number of Centrality algorithms are suggested to detect essential proteins, but they focus only on network structures. In this paper, we develop a new Centrality algorithm, named MCGO which uses network motifs for Centrality Measure in the graph pruned by EDGE GO. EDGE GO algorithm utilizes Gene Ontology(GO) to trim a number of uninformative edges from the network. We compare the performance of our algorithm with DC (degree Centrality) and SoECC (sum of edge clustering coefficient) against various evaluation Measures. Experimental results applied to an yeast protein-protein interaction network downloaded from DIP database show that MCGO performs significantly better than DC and SoECC. We also show that DC and SoECC improve greatly when EDGE GO is applied to them.
Liaquat Hossain - One of the best experts on this subject based on the ideXlab platform.
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New Direction in Degree Centrality Measure: Towards a Time-Variant Approach
International Journal of Information Technology and Decision Making, 2014Co-Authors: Shahadat Uddin, Liaquat Hossain, Rolf T. WigandAbstract:Degree Centrality is considered to be one of the most basic Measures of social network analysis, which has been used extensively in diverse research domains for measuring network positions of actors in respect of the connections with their immediate neighbors. In network analysis, it emphasizes the number of connections that an actor has with others. However, it does not accommodate the value of the duration of relations with other actors in a network; and, therefore, this traditional degree Centrality approach regards only the presence or absence of links. Here, we introduce a time-variant approach to the degree Centrality Measure — time scale degree Centrality (TSDC), which considers both presence and duration of links among actors within a network. We illustrate the difference between traditional and TSDC Measure by applying these two approaches to explore the impact of degree attributes of a patient-physician network evolving during patient hospitalization periods on the hospital length of stay (LOS) ...
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time scale degree Centrality a time variant approach to degree Centrality Measures
Advances in Social Networks Analysis and Mining, 2011Co-Authors: Shahadat Uddin, Liaquat HossainAbstract:In this paper, we introduce a time-variant approach to degree Centrality Measure - time scale degree Centrality (TSDC), which considers both presence and duration of links among actors within a network, whereas, the traditional degree Centrality approach regards only the presence or absence of links. We illustrate the difference between traditional and time scale degree Centrality Measure by applying these two approaches to explore the impact of 'degree' attributes of doctor-patient network that evolves during patient hospitalization period on the hospital length of stay (LOS) both in macro- and micro-level. In macro-level, both the traditional and time-scale approaches to degree Centrality can explain the relationship between the 'degree' attribute of doctor-patient network and LOS. However, at micro-level or small cluster level, TSDC provides better explanation while traditional degree Centrality approach is impotent to explain the relationship between them.
Vicki S Hertzberg - One of the best experts on this subject based on the ideXlab platform.
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dynamic communicability and epidemic spread a case study on an empirical dynamic contact network
Journal of Complex Networks, 2016Co-Authors: Isabel Chen, Michele Benzi, Howard H Chang, Vicki S HertzbergAbstract:We analyse a recently proposed temporal Centrality Measure applied to an empirical network based on person-to-person contacts in an emergency department of a busy urban hospital. We show that temporal Centrality identifies a distinct set of top-spreaders than Centrality based on the time-aggregated binarized contact matrix, so that taken together, the accuracy of capturing top-spreaders improves significantly. However, with respect to predicting epidemic outcome, the temporal Measure does not necessarily outperform less complex Measures. Our results also show that other temporal markers such as duration observed and the time of first appearance in the network can be used in a simple predictive model to generate predictions that capture the trend of the observed data remarkably well.
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dynamic communicability and epidemic spread a case study on an empirical dynamic contact network
arXiv: Physics and Society, 2016Co-Authors: Isabel Chen, Michele Benzi, Howard H Chang, Vicki S HertzbergAbstract:We analyze a recently proposed temporal Centrality Measure applied to an empirical network based on person-to-person contacts in an emergency department of a busy urban hospital. We show that temporal Centrality identifies a distinct set of top-spreaders than Centrality based on the time-aggregated binarized contact matrix, so that taken together, the accuracy of capturing top-spreaders improves significantly. However, with respect to predicting epidemic outcome, the temporal Measure does not necessarily outperform less complex Measures. Our results also show that other temporal markers such as duration observed and the time of first appearance in the the network can be used in a simple predictive model to generate predictions that capture the trend of the observed data remarkably well.
Hanhui Zhang - One of the best experts on this subject based on the ideXlab platform.
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a new essential protein discovery method based on the integration of protein protein interaction and gene expression data
BMC Systems Biology, 2012Co-Authors: Hanhui Zhang, Jianxin Wang, Y PanAbstract:Background: Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins’ essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based Centrality Measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results: In this paper, we propose a new Centrality Measure, named PeC, based on the integration of proteinprotein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed Centrality Measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized a-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic Centrality Measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins. Conclusions: We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new Centrality Measure, PeC, is an effective essential protein discovery method.
Jianxin Wang - One of the best experts on this subject based on the ideXlab platform.
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identifying essential proteins via integration of protein interaction and gene expression data
Bioinformatics and Biomedicine, 2012Co-Authors: Xiwei Tang, Jianxin Wang, Y PanAbstract:Essential proteins are vital for an organism's viability under a variety of conditions. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency. In this work, Pearson correlation coefficient (PCC) is used to bridge the gap between PPI and gene expression data. Based on PCC and Edge Clustering Coefficient (ECC), a new Centrality Measure, i.e., the weighted degree Centrality (WDC), is developed to achieve the reliable prediction of essential proteins. WDC is employed to identify essential proteins in the yeast PPI network in order to estimate its performance. For comparison, other prediction technologies are also performed to identify essential proteins. Some evaluation methods are used to analyze the results from various prediction approaches. The analyses prove that WDC outperforms other state-of-the-art ones. At the same time, the analyses also mean that it is an effective way to predict essential proteins by means of integrating different data sources.
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a new essential protein discovery method based on the integration of protein protein interaction and gene expression data
BMC Systems Biology, 2012Co-Authors: Hanhui Zhang, Jianxin Wang, Y PanAbstract:Background: Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins’ essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based Centrality Measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results: In this paper, we propose a new Centrality Measure, named PeC, based on the integration of proteinprotein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed Centrality Measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized a-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic Centrality Measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins. Conclusions: We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new Centrality Measure, PeC, is an effective essential protein discovery method.
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essential protein discovery based on network motif and gene ontology
Bioinformatics and Biomedicine, 2011Co-Authors: Wooyoung Kim, Jianxin Wang, Y PanAbstract:Essential proteins are indispensable to support cellular life and constitute a minimal set required for a living cell. Fast progress in high-throughput technologies and large amount of data enable to discover essential proteins in system level by analyzing protein-protein interaction networks. A number of Centrality algorithms are suggested to detect essential proteins, but they focus only on network structures. In this paper, we develop a new Centrality algorithm, named MCGO which uses network motifs for Centrality Measure in the graph pruned by EDGE GO. EDGE GO algorithm utilizes Gene Ontology(GO) to trim a number of uninformative edges from the network. We compare the performance of our algorithm with DC (degree Centrality) and SoECC (sum of edge clustering coefficient) against various evaluation Measures. Experimental results applied to an yeast protein-protein interaction network downloaded from DIP database show that MCGO performs significantly better than DC and SoECC. We also show that DC and SoECC improve greatly when EDGE GO is applied to them.