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1,642 results on '"WU Xudong"'

62. Insights into the regulation of human CNV-miRNAs from the view of their target genes

Author

Wu Xudong, Zhang Dinglin, and Li Guohui

Subjects
Copy number variation, miRNA, Expression variation, HapMap ethnic population, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background microRNAs (miRNAs) represent a class of small (typically 22 nucleotides in length) non-coding RNAs that can degrade their target mRNAs or block their translation. Recent research showed that copy number alterations of miRNAs and their target genes are highly prevalent in cancers; however, the evolutionary and biological functions of naturally existing copy number variable miRNAs (CNV-miRNAs) among individuals have not been studied extensively throughout the genome. Results In this study, we comprehensively analyzed the properties of genes regulated by CNV-miRNAs, and found that CNV-miRNAs tend to target a higher average number of genes and prefer to synergistically regulate the same genes; further, the targets of CNV-miRNAs tend to have higher variability of expression within and between populations. Finally, we found the targets of CNV-miRNAs are more likely to be differentially expressed among tissues and developmental stages, and participate in a wide range of cellular responses. Conclusions Our analyses of CNV-miRNAs provide new insights into the impact of copy number variations on miRNA-mediated post-transcriptional networks. The deeper interpretation of patterns of gene expression variation and the functional characterization of CNV-miRNAs will help to broaden the current understanding of the molecular basis of human phenotypic diversity.

Published
2012
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63. Preferential regulation of miRNA targets by environmental chemicals in the human genome

Author

Wu Xudong and Song Yijiang

Subjects
Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background microRNAs (miRNAs) represent a class of small (typically 22 nucleotides in length) non-coding RNAs that can degrade their target mRNAs or block their translation. Recent disease research showed the exposure to some environmental chemicals (ECs) can regulate the expression patterns of miRNAs, which raises the intriguing question of how miRNAs and their targets cope with the exposure to ECs throughout the genome. Results In this study, we comprehensively analyzed the properties of genes regulated by ECs (EC-genes) and found miRNA targets were significantly enriched among the EC-genes. Compared with the non-miRNA-targets, miRNA targets were roughly twice as likely to be EC-genes. By investigating the collection methods and other properties of the EC-genes, we demonstrated that the enrichment of miRNA targets was not attributed to either the potential collection bias of EC-genes, the presence of paralogs, longer 3'UTRs or more conserved 3'UTRs. Finally, we identified 1,842 significant concurrent interactions between 407 miRNAs and 497 ECs. This association network of miRNAs-ECs was highly modular and could be separated into 14 interconnected modules. In each module, miRNAs and ECs were closely connected, providing a good method to design accurate miRNA markers for ECs in toxicology research. Conclusions Our analyses indicated that miRNAs and their targets played important roles in cellular responses to ECs. Association analyses of miRNAs and ECs will help to broaden the understanding of the pathogenesis of such chemical components.

Published
2011
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64. Genes encoding hub and bottleneck enzymes of the Arabidopsis metabolic network preferentially retain homeologs through whole genome duplication

Author

Qi Xiaoquan and Wu Xudong

Subjects
Evolution, QH359-425
Abstract

Abstract Background Whole genome duplication (WGD) occurs widely in angiosperm evolution. It raises the intriguing question of how interacting networks of genes cope with this dramatic evolutionary event. Results In study of the Arabidopsis metabolic network, we assigned each enzyme (node) with topological centralities (in-degree, out-degree and between-ness) to measure quantitatively their centralities in the network. The Arabidopsis metabolic network is highly modular and separated into 11 interconnected modules, which correspond well to the functional metabolic pathways. The enzymes with higher in-out degree and between-ness (defined as hub and bottleneck enzymes, respectively) tend to be more conserved and preferentially retain homeologs after WGD. Moreover, the simultaneous retention of homeologs encoding enzymes which catalyze consecutive steps in a pathway is highly favored and easily achieved, and enzyme-enzyme interactions contribute to the retention of one-third of WGD enzymes. Conclusions Our analyses indicate that the hub and bottleneck enzymes of metabolic network obtain great benefits from WGD, and this event grants clear evolutionary advantages in adaptation to different environments.

Published
2010
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82. A causal association of ANKRD37 with human hippocampal volume

Author

Xu, Jiayuan, Xia, Xianyou, Li, Qiaojun, Dou, Yan, Suo, Xinjun, Sun, Zuhao, Liu, Nana, Han, Yating, Sun, Xiaodi, He, Yukun, Qin, Wen, Zhang, Shijie, Banaschewski, Tobias, Flor, Herta, Grigis, Antoine, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Artiges, Eric, Nees, Frauke, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Walter, Henrik, Sham, Pak Chung, Schumann, Gunter, Wu, Xudong, Li, Mulin Jun, and Yu, Chunshui

Published
2022
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90. BLAG: Bandit On Large Action Set Graph

Author

Lu, Yucheng, Wu, Xudong, Meng, Jingfan, Fu, Luoyi, and Wang, Xinbing

Subjects
Computer Science - Social and Information Networks
Abstract

Information diffusion in social networks facilitates rapid and large-scale propagation of content. However, spontaneous diffusion behavior could also lead to the cascading of sensitive information, which is neglected in prior arts. In this paper, we present the first look into adaptive diffusion of sensitive information, which we aim to prevent from widely spreading without incurring much information loss. We undertake the investigation in networks with partially known topology, meaning that some users' ability of forwarding information is unknown. Formulating the problem into a bandit model, we propose BLAG (Bandit on Large Action set Graph), which adaptively diffuses sensitive information towards users with weak forwarding ability that is learnt from tentative transmissions and corresponding feedbacks. BLAG enjoys a low complexity of O(n), and is provably more efficient in the sense of half regret bound compared with prior learning method. Experiments on synthetic and three real datasets further demonstrate the superiority of BLAG in terms of adaptive diffusion of sensitive information over several baselines, with at least 40 percent less information loss, at least 10 times of learning efficiency given limited learning rounds and significantly postponed cascading of sensitive information.

Published
2018

91. Evolving Influence Maximization in Evolving Networks

Author

Wu, Xudong, Fu, Luoyi, Zhang, Zixin, Meng, Jingfan, Wang, Xinbing, and Chen, Guihai

Subjects
Computer Science - Social and Information Networks
Abstract

Influence Maximization (IM) aims to maximize the number of people that become aware of a product by finding the `best' set of `seed' users to initiate the product advertisement. Unlike prior arts on static social networks containing fixed number of users, we undertake the first study of IM in more realistic evolving networks with temporally growing topology. The task of evolving IM ({\bfseries EIM}), however, is far more challenging over static cases in the sense that seed selection should consider its impact on future users and the probabilities that users influence one another also evolve over time. We address the challenges through $\mathbb{EIM}$, a newly proposed bandit-based framework that alternates between seed nodes selection and knowledge (i.e., nodes' growing speed and evolving influences) learning during network evolution. Remarkably, $\mathbb{EIM}$ involves three novel components to handle the uncertainties brought by evolution

Published
2018

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