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Metagenomic Surveys of Gut Microbiota 被引量:11
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作者 Rahul Shubhra Mandal Sudipto Saha Santasabuj Das 《Genomics, Proteomics & Bioinformatics》 SCIE CAS CSCD 2015年第3期148-158,共11页
Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat... Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat, and diet. Recently, culture-independent sequencing techniques have added a new dimension to the study of gut microbiota and the challenge to analyze the large volume of sequencing data is increasingly addressed by the development of novel computational tools and methods. Interestingly, gut microbiota maintains a constant relative abundance at operational tax- onomic unit (OTU) levels and altered bacterial abundance has been associated with complex diseases such as symptomatic atherosclerosis, type 2 diabetes, obesity, and colorectal cancer. Therefore, the study of gut microbial population has emerged as an important field of research in order to ulti- mately achieve better health. In addition, there is a spontaneous, non-linear, and dynamic interac- tion among different bacterial species residing in the gut. Thus, predicting the influence of perturbed microbe-microbe interaction network on health can aid in developing novel therapeutics. Here, we summarize the population abundance of gut microbiota and its variation in different clinical states, computational tools available to analyze the pyrosequencing data, and gut microbe-microbe inter- action networks. 展开更多
关键词 DISEASE SEQUENCING 16S rRNA Operational taxonomic unit Microbial interactionnetwork
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New insights into the interplay between long non-coding RNAs and RNA-binding proteins in cancer 被引量:33
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作者 Zi-Ting Yao Yan-Ming Yang +4 位作者 Miao-Miao Sun Yan He Long Liao Kui-Sheng Chen Bin Li 《Cancer Communications》 SCIE 2022年第2期117-140,共24页
With the development of proteomics and epigenetics,a large number of RNA-binding proteins(RBPs)have been discovered in recent years,and the inter-action between long non-coding RNAs(lncRNAs)and RBPs has also received ... With the development of proteomics and epigenetics,a large number of RNA-binding proteins(RBPs)have been discovered in recent years,and the inter-action between long non-coding RNAs(lncRNAs)and RBPs has also received increasing attention.It is extremely important to conduct in-depth research on the lncRNA-RBP interaction network,especially in the context of its role in the occurrence and development of cancer.Increasing evidence has demonstrated that lncRNA-RBP interactions play a vital role in cancer progression;there-fore,targeting these interactions could provide new insights for cancer drug discovery.In this review,we discussed how lncRNAs can interact with RBPs to regulate their localization,modification,stability,and activity and discussed the effects of RBPs on the stability,transport,transcription,and localization of lncRNAs.Moreover,we explored the regulation and influence of these inter-actions on lncRNAs,RBPs,and downstream pathways that are related to can-cer development,such as N6-methyladenosine(m6A)modification of lncRNAs.In addition,we discussed how the lncRNA-RBP interaction network regulates cancer cell phenotypes,such as proliferation,apoptosis,metastasis,drug resis-tance,immunity,tumor environment,and metabolism.Furthermore,we sum-marized the therapeutic strategies that target the lncRNA-RBP interaction net-work.Although these treatments are still in the experimental stage and various theories and processes are still being studied,we believe that these strategiesmay provide new ideas for cancer treatment. 展开更多
关键词 cancer epigenetics CANCER interactionnetwork lncRNA-RBP longnon-codingRNA RNA-binding protein treatmentstrategy
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网络结构化多Agent系统的任务分配 被引量:9
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作者 蒋嶷川 《模式识别与人工智能》 EI CSCD 北大核心 2012年第2期262-272,共11页
网络结构化多Agent系统既包括系统运行的底层物理网络,还包括Agent之间的交互网络.传统的任务分配方式并没有深入考虑到网络结构化的特点.文中首先论述网络结构化多Agent系统中任务分配的特点,介绍和分析基于底层网络拓扑与资源分布的... 网络结构化多Agent系统既包括系统运行的底层物理网络,还包括Agent之间的交互网络.传统的任务分配方式并没有深入考虑到网络结构化的特点.文中首先论述网络结构化多Agent系统中任务分配的特点,介绍和分析基于底层网络拓扑与资源分布的任务分配方式、基于Agent交互网络与资源分布的任务分配方式和基于综合网络情境资源的任务分配方式.然后对相关工作进行综述,并与网络结构化多Agent系统任务分配模型进行比较分析.最后论述该方向的难点和未来要解决的问题. 展开更多
关键词 多AGENT系统 网络结构化 任务分配 底层网络 交互网络
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