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| 1 | Finding MicroRNA Targets in Plants: Current Status and Perspectives显示文摘MicroRNAs (miRNAs), a class of 20-24 nt long non-coding RNAs, have critical roles in diverse biological processes including development, proliferation, stress response, etc. With the development and availability of experimental technologies and computational approaches, the field of miRNA biology has advanced tremendously over the last decade. By sequence complementarity, miRNAs have been estimated to regulate certain mRNA transcripts. Although it was once thought to be simple and straightforward to find plant miR-NA targets, this viewpoint is being challenged by genetic and biochemical studies. In this review, we summarize recent progress in plant miRNA target recognition mechanisms, principles of target prediction, and introduce current experimental and computational tools for plant miRNA target prediction. At the end, we also present our thinking on the outlook for future directions in the development of plant miRNA target finding methods. | Jiandong Ding Shuigeng Zhou Jihong Guan | 2012 | Genomics, Proteomics & Bioinformatics2012，10，5： | 4 |
| 2 | CloudNMF:A MapReduce Implementation of Nonnegative Matrix Factorization for Large-scale Biological Datasets显示文摘In the past decades,advances in high-throughput technologies have led to the generation of huge amounts of biological data that require analysis and interpretation.Recently,nonnegative matrix factorization(NMF) has been introduced as an efficient way to reduce the complexity of data as well as to interpret them,and has been applied to various fields of biological research.In this paper,we present CloudNMF,a distributed open-source implementation of NMF on a MapReduce framework.Experimental evaluation demonstrated that CloudNMF is scalable and can be used to deal with huge amounts of data,which may enable various kinds of a high-throughput biological data analysis in the cloud.CloudNMF is freely accessible at http://gffzz34e1a21ecaa241cchfnp0pwpqck0b6w06.ffgz.tsg.suse.edu.cn/projects/CloudNMF.html. | Ruiqi Liao Yifan Zhang Jihong Guan Shuigeng Zhou | 2014 | Genomics, Proteomics & Bioinformatics2014，12，1： | 2 |
| 3 | Evolving apollonian networks with small-world scale-free topologies显示文摘 | Zhongzhi Zhang Lili Rong Shuigeng Zhou | 2006 | Physical Review E2006，74，04： | 1 |
| 4 | To- wards effective document clustering: a constrained K-means based approach 显示文摘 | HUA Guobiao ZHOU Shuigeng GUAN Jihong | 2008 | Information Processing & Management2008，44，4： | 1 |
| 5 | Towards ef- fective document clustering: A constrained K-means based ap- proach 显示文摘 | Hua Guobiao Zhou Shuigeng Guan Jihjong el al | 2008 | Information Processing & Management2008，44，4： | 1 |
| 6 | Distributed localization using a moving beacon in wireless sensor net- works显示文摘 | XIAO Bin CHEN Hekang ZHOU Shuigeng | 2008 | IEEE Transactions on Parallel and Distributed Systems2008，19，5： | 1 |
| 7 | Fusion Analysis of Resting-State Networks and Its Application to Alzheimer's Disease显示文摘Functional networks are extracted from resting-state functional magnetic resonance imaging data to explore the biomarkers for distinguishing brain disorders in disease diagnosis. Previous works have primarily focused on using a single Resting-State Network(RSN) with various techniques. Here, we apply fusion analysis of RSNs to capturing biomarkers that can combine the complementary information among the RSNs. Experiments are carried out on three groups of subjects, i.e., Cognition Normal(CN), Early Mild Cognitive Impairment(EMCI), and Alzheimer's Disease(AD) groups, which correspond to the three progressing stages of AD; each group contains18 subjects. First, we apply group Independent Component Analysis(ICA) to extracting the Default Mode Network(DMN) and Dorsal Attention Network(DAN) for each subject group. Then, by obtaining the common DMN and DAN as templates for each group, we employ the individual ICA to extract the DMN and DAN for each subject.Finally, we fuse the DMNs and DANs to explore the biomarkers. The results show that(1) the templates generated by group ICA can extract the RSN for each subject by individual ICA effectively;(2) the RSNs combined with the fusion analysis can obtain more informative biomarkers than without fusion analysis;(3) the most different regions of DMN and DAN are found between CN and EMCI and between EMCI and AD, which show differences. For the DMN, the difference in the medial prefrontal cortex between the EMCI and AD is smaller than that between CN and EMCI, whereas that in the posterior cingulate between EMCI and AD is larger. As for the DAN, the difference in the intraparietal sulcus is smaller than that between CN and EMCI;(4) extracting DMN and DAN for each subject via the back reconstruction of group ICA is invalid. | Shengbing Pei Jihong Guan Shuigeng Zhou | 2019 | Tsinghua Science and Technology2019，24，4： | 1 |
| 8 | Effects of accelerating growth on theevolution of weighted complex networks 显示文摘 | Zhongzhi Zhang Lujun Fang Shuigeng Zhou Jihong Guan | 2009 | Physica A: Statistical Mechanics and its Applications2009，388，23： | 1 |
| 9 | Enhancing Routing Robustness of Unstructured Peer-to-Peer Net- works Using Mobile Agents显示文摘 | Xu Ming Zhou Shuigeng Guan Jihong | 2012 | Journal of Network and Systems Management2012，20，3： | 1 |
| 10 | A lightweight multidimensional index for complex queries over DHTs显示文摘 | Tang Yuzhe Xu Jianliang Zhou Shuigeng Lee Wangchien | 2011 | IEEE Transactions onParallel and Distributed Systems2011，22，12： | 1 |
| 11 | A RAMCloud Storage System based on HDFS: Architecture, implementation and evaluation显示文摘 | Yifeng Luo Siqiang Luo Jihong Guan Shuigeng Zhou | 2012 | The Journal of Systems & Software2012，，： | 1 |
| 12 | Gene Ontology Based Transfer Learning for Protein Subcellular Localization显示文摘 | Mei Shuyu Wang Fei Zhou Shuigeng | 2011 | BMC Bioinformatics2011，12，1： | 1 |
| 13 | Moble- Agent Based Distributed WebGIS 显示文摘 | GUAN Jihong ZHOU Shuigeng ZHOU Aoying | 2011 | Lecture Notes in Computer Science2011，，2172： | 1 |
| 14 | From regular to growing small -world networks显示文摘 | ZHONGZHI ZHANG SHUIGENG ZHOU ZHEN SHEN | 2007 | Physica A2007，，385： | 1 |
| 15 | Local-world evolving networks with tunable clustering显示文摘 | Zhongzhi Zhang Lili Rong Bing Wang Shuigeng Zhou Jihong Guan | 2007 | Physica A2007，380，： | 1 |
| 16 | Distributed localization using a moving beacon in wireless sensor networks显示文摘 | XIAO Bin CHEN Hekang ZHOU Shuigeng | 2008 | IEEE Transactions on Parallel and Distributed Systems2008，19，4： | 1 |
| 17 | Efficient top-k processing in large-scaled distributed environments显示文摘 | ZHAO Keping TAO Yufei ZHOU Shuigeng | 2007 | Data and Knowledge Engineering2007，63，2： | 1 |
| 18 | Distributed Locali zation Using a Moving Beacon in Wireless Sensor Networks 显示文摘 | Bin Xiao Hekang Chen Shuigeng Zhou | 2008 | IEEE Transactions on Parallel and Distributed System2008，19，5： | 1 |
| 19 | Deterministic weighted scale-free small-world networks显示文摘 | Zhang Yichao Zhang Zhongzhi Zhou Shuigeng | 2010 | Physica A2010，389，16： | 1 |
| 20 | Distributed localization using a moving beacon in Wireless Sensor Networks显示文摘 | Xiao Bin Chen Hekang Zhou Shuigeng | 2008 | IEEE Transactions on Parallel and Distributed Systems2008，19，5： | 1 |
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