The Experts below are selected from a list of 160017 Experts worldwide ranked by ideXlab platform

Yuan'an Liu - One of the best experts on this subject based on the ideXlab platform.

  • MASS - Analysis of mobile WiMAX security: Vulnerabilities and solutions
    2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, 2008
    Co-Authors: Tao Han, Ning Zhang, Kaiming Liu, Bihua Tang, Yuan'an Liu
    Abstract:

    In this paper, we first give an overview of security architecture of mobile WiMAX Network. Then, we investigate man-in-the-middle attacks and Denial of Service (DoS) attacks toward 802.16e-based Mobile WiMAX Network. We find the Initial Network procedure is not effectively secured that makes Man-in-the-middle and Dos attacks possible. In addition, we find the resource saving and handover procedure is not secured enough to resist DoS attacks. Focusing on these two kinds of attacks, we propose Secure Initial Network Entry Protocol (SINEP) based on Diffie-Hellman (DH) key exchange protocol to enhance the security level during Network Initial. We modify DH key exchange protocol to fit it into mobile WiMAX Network as well as to eliminate existing weakness in original DH key exchange protocol.

Jorg Menche - One of the best experts on this subject based on the ideXlab platform.

  • integration of molecular interactome and targeted interaction analysis to identify a copd disease Network module
    Scientific Reports, 2018
    Co-Authors: Amitabh Sharma, Maksim Kitsak, Michael H Cho, Asher Ameli, Xiaobo Zhou, Zhiqiang Jiang, James D Crapo, Terri H Beaty, Jorg Menche
    Abstract:

    The polygenic nature of complex diseases offers potential opportunities to utilize Network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract Network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease Network module. We identified an Initial disease Network neighborhood by applying a random-walk method. Next, we developed a Network-based closeness approach (CAB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the Initial Network neighborhood. Moreover, compared to a similar method (local radiality), the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the Network-based closeness approach were combined with the Initial Network neighborhood to build a comprehensive disease Network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related Network components using new laboratory data to overcome incompleteness of the current interactome.

  • integration of molecular interactome and targeted interaction analysis to identify a copd disease Network module
    bioRxiv, 2018
    Co-Authors: Amitabh Sharma, Maksim Kitsak, Michael H Cho, Asher Ameli, Xiaobo Zhou, Zhiqiang Jiang, James D Crapo, Terri H Beaty, Jorg Menche
    Abstract:

    Abstract The polygenic nature of complex diseases offers potential opportunities to utilize Network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract Network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with new experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease Network module. We identified an Initial disease Network neighborhood by applying a random-walk method. Next, we developed a Network-based closeness approach (CAB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the Initial Network neighborhood. Moreover, compared to a similar method (local radiality), the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the Network-based closeness approach were combined with the Initial Network neighborhood to build a comprehensive disease Network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related Network components using new laboratory data to overcome incompleteness of the current interactome.

Asher Ameli - One of the best experts on this subject based on the ideXlab platform.

  • integration of molecular interactome and targeted interaction analysis to identify a copd disease Network module
    Scientific Reports, 2018
    Co-Authors: Amitabh Sharma, Maksim Kitsak, Michael H Cho, Asher Ameli, Xiaobo Zhou, Zhiqiang Jiang, James D Crapo, Terri H Beaty, Jorg Menche
    Abstract:

    The polygenic nature of complex diseases offers potential opportunities to utilize Network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract Network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease Network module. We identified an Initial disease Network neighborhood by applying a random-walk method. Next, we developed a Network-based closeness approach (CAB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the Initial Network neighborhood. Moreover, compared to a similar method (local radiality), the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the Network-based closeness approach were combined with the Initial Network neighborhood to build a comprehensive disease Network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related Network components using new laboratory data to overcome incompleteness of the current interactome.

  • integration of molecular interactome and targeted interaction analysis to identify a copd disease Network module
    bioRxiv, 2018
    Co-Authors: Amitabh Sharma, Maksim Kitsak, Michael H Cho, Asher Ameli, Xiaobo Zhou, Zhiqiang Jiang, James D Crapo, Terri H Beaty, Jorg Menche
    Abstract:

    Abstract The polygenic nature of complex diseases offers potential opportunities to utilize Network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract Network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with new experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease Network module. We identified an Initial disease Network neighborhood by applying a random-walk method. Next, we developed a Network-based closeness approach (CAB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the Initial Network neighborhood. Moreover, compared to a similar method (local radiality), the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the Network-based closeness approach were combined with the Initial Network neighborhood to build a comprehensive disease Network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related Network components using new laboratory data to overcome incompleteness of the current interactome.

Turgay Yilmaz - One of the best experts on this subject based on the ideXlab platform.

  • LCN - Autonomous deployment of sensors for maximized coverage and guaranteed connectivity in Underwater Acoustic Sensor Networks
    38th Annual IEEE Conference on Local Computer Networks, 2013
    Co-Authors: Fatih Senel, Kemal Akkaya, Turgay Yilmaz
    Abstract:

    Self-deployment of sensors with maximized coverage in Underwater Acoustic Sensor Networks (UWASNs) is challenging due to difficulty of access to 3-D underwater environments. The problem is further compounded if the connectivity of the final Network is required. One possible approach is to drop the sensors on the surface and then move them to certain depths in the water to maximize the 3-D coverage while maintaining the connectivity. In this paper, we propose a purely distributed node deployment scheme for UWASNs which only requires random dropping of sensors on the water surface. The goal is to expand the Initial Network to 3-D with maximized coverage and guaranteed connectivity with a surface station. The idea is based on determining the connected dominating set of the Initial Network and then adjust the depths of all dominatee and dominator neighbors of a particular dominator node for minimizing the coverage overlaps among them while still keeping the connectivity with the dominator. The process starts with a leader node and spans all the dominators in the Network for repositioning. Simulations results indicate that connectivity can be guaranteed regardless of the transmission and sensing range ratio with a coverage very close to a coverage-aware deployment approach.

Lizhi Zhang - One of the best experts on this subject based on the ideXlab platform.