期刊：
2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM),2013年:386-391 ISSN：2156-1125
通讯作者：
Zhao, Junmin
作者机构：
[Zhao, Junmin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[He, Tingting; Hu, Xiaohua; Li, Peng; XianjunShen; Zhang, Ming] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Engn, Philadelphia, PA USA.;[Zhao, Junmin] Henan Univ Urban Construct, Inst Comp Sci & Engn, Pingdingshan, Peoples R China.
通讯机构：
[Zhao, Junmin] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)
会议时间：
DEC 18-21, 2013
会议地点：
Shanghai, PEOPLES R CHINA
会议主办单位：
[Zhao, Junmin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.^[Hu, Xiaohua;He, Tingting;Li, Peng;Zhang, Ming;XianjunShen] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Engn, Philadelphia, PA USA.^[Zhao, Junmin] Henan Univ Urban Construct, Inst Comp Sci & Engn, Pingdingshan, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
Protein Complex;Gene co-express;Biological network;Weighted PPI network
摘要：
Recent studies have shown that protein complex is composed of core and attachment proteins, and proteins inside the core are highly co-expressed. Based on this new concept, we reconstruct weighted PPI network by using gene expression data, and develop a novel protein complex identification algorithm from the angle of edge(PCIA-GeCo). First, we select the edge with high co-expressed coefficient as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the weighted density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins for each core. A comprehensive comparison in term of F-measure, Coverage rate between our method and three other existing algorithms HUNTER, COACH and CORE has been made by comparing the predicted complexes against benchmark complexes. The evaluation results show our method PCIA-GeCo is effective; it can identify protein complexes more accurately.
期刊：
International Journal of Multimedia and Ubiquitous Engineering,2013年8(2):123-142 ISSN：1975-0080
通讯作者：
Hu, R.(hrm1964@163.com)
作者机构：
[Hu, Ruimin; Yang, Hongyun] Computer School, Wuhan University, Hubei, 430079, China;[Hu, Ruimin] National Engineering Research Center for Multimedia Software, Wuhan University, Hubei, 430079, China;[Yang, Hongyun] National Engineering Research Center for E-Learning, Central China Normal University, Hubei, 430072, China
摘要：
Peer-to-Peer (P2P) streaming is being considered as the most promising approach to deliver real-time video to large scale users over the Internet. Neighbor selection is one of the key components to construct overlay topology for P2P streaming systems. Currently the majority of QoS-aware neighbor selection approaches assume that the allocated bandwidth resources to individual peers are proportional to their incoming bandwidth rather than their outgoing bandwidth and don't suit for bandwidth resource scarce environments. In this paper, we incorporate taxation-based incentive mechanism into QoS aware neighbor selection method to computer the allocated number of neighbors of peers. The main contribution of this paper is: i) we incorporate linear taxation-model into SVC-based layered media delivery to determined the connection number of peers and propose a distributed bandwidth resource allocation policy; ii) when selecting neighbors, it considers existing peers' uplink capacity and source to peer's delay as a whole to handle long cumulate delay caused by bandwidth aggregation in mesh-based system. Simulation results demonstrate that under resource constraint scene, our proposed method can receive good performance compared with fixed random neighbor selection method and QoS-aware method based on bandwidth-latency ratio on the metric of the chunk loss rate, the average delivery delay, control overhead and PSNR.
作者机构：
[Chen, Dan; Wang, Lizhe] China Univ Geosci, Sch Comp, Wuhan 430074, Peoples R China.;[Wang, Lizhe] Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing 100864, Peoples R China.;[Streit, Achim; Tao, Jie; Marten, Holger] Karlsruhe Inst Technol, Steinbuch Ctr Comp, D-76021 Karlsruhe, Germany.;[Ranjan, Rajiv] CSIRO, ICT Ctr, Informat Engn Lab, Canberra, ACT, Australia.;[Chen, Jingying] Cent China Normal Univ, Natl Engn Ctr E Learning, Beijing, Peoples R China.
通讯机构：
[Wang, Lizhe] C;Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing 100864, Peoples R China.
关键词：
Cloud computing;Data-intensive computing;Hadoop;MapReduce;Massive data processing
摘要：
Recently, the computational requirements for large-scale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data is processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In this paper, we present the design and implementation of G-Hadoop, a MapReduce framework that aims to enable large-scale distributed computing across multiple clusters.
期刊：
International Journal of Multimedia and Ubiquitous Engineering,2013年8(6):377-386 ISSN：1975-0080
作者机构：
[Zhu, Xiaoliang; Yang, Hongyun] National Engineering Research Center for E-learning, Central China Normal University, Hubei, 430079, China;[Chen, Xuhui] College of Mathematics and Computer Science, Wuhan Textile University, Hubei, 430070, China
关键词：
Bandwidth resource allocation;P2P lives streaming;Relative urgency of playback;Supplier side scheduler
