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gene functional module
相关语句
  基因功能模块
     Given the phenomenon of gene functional modulization in a cell, the concepts of gene functional module and characteristic functional module were formally defined.
     针对细胞内基因功能模块化的现象,定义了“基因功能模块”和“特征功能模块”两个概念,并基于这两个概念提出一种“与实验条件相关的基因功能模块聚类算法”。
短句来源
     CONDITION-RELATED GENE FUNCTIONAL MODULE CLUSTER ANALYSIS
     与实验条件相关的基因功能模块聚类分析方法
短句来源
     In view of these two novel concepts, the condition-related gene functional module clustering algorithm was developed, which clusters genes into conditional-related gene functional modules based on both gene function knowledge and gene expression data.
     该算法综合利用基因功能知识与基因表达谱信息,将基因聚类为与实验条件相关的基因功能模块
短句来源
  “gene functional module”译为未确定词的双语例句
     It was demonstrated with the adding-noise experiment that the condition-related gene functional module clustering algorithm was superior to both hierarchical clustering and fuzzy C-means clustering with respect to their stability against data noise that was commonly found in microarray technology.
     加噪音实验显示,在基因芯片技术可能发生的噪音范围内,该算法对噪音的稳健性优于层次聚类和模糊C均值聚类。
短句来源
  相似匹配句对
     Functional Redundancy of Gene.
     基因的功能冗余
短句来源
     Functional Research of LASS2 Gene
     人源性长寿保障基因LASS2的功能研究
短句来源
     Clustering of Gene Expression Profiles Based on Functional Modules
     基于功能模块的基因表达谱聚类分析
短句来源
     CONDITION-RELATED GENE FUNCTIONAL MODULE CLUSTER ANALYSIS
     与实验条件相关的基因功能模块聚类分析方法
短句来源
     On Gene Patenting
     基因的专利问题
短句来源
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Given the phenomenon of gene functional modulization in a cell, the concepts of gene functional module and characteristic functional module were formally defined. In view of these two novel concepts, the condition-related gene functional module clustering algorithm was developed, which clusters genes into conditional-related gene functional modules based on both gene function knowledge and gene expression data. Increasing level of artificial noise was added into...

Given the phenomenon of gene functional modulization in a cell, the concepts of gene functional module and characteristic functional module were formally defined. In view of these two novel concepts, the condition-related gene functional module clustering algorithm was developed, which clusters genes into conditional-related gene functional modules based on both gene function knowledge and gene expression data. Increasing level of artificial noise was added into the original gene expression dataset and the stability of various functional modules was compared, and those functional modules which were most resistant to the data noise were extracted as the characteristic functional modules. It was demonstrated with the adding-noise experiment that the condition-related gene functional module clustering algorithm was superior to both hierarchical clustering and fuzzy C-means clustering with respect to their stability against data noise that was commonly found in microarray technology. Eight characteristic functional modules were extracted when the algorithm was applied to NCI60 gene expression data.

针对细胞内基因功能模块化的现象,定义了“基因功能模块”和“特征功能模块”两个概念,并基于这两个概念提出一种“与实验条件相关的基因功能模块聚类算法”。该算法综合利用基因功能知识与基因表达谱信息,将基因聚类为与实验条件相关的基因功能模块。向基因表达谱中加入水平逐渐升高的数据噪音,根据基因功能模块对数据噪音的抵抗力,确定最稳定的基因功能模块,即特征功能模块。加噪音实验显示,在基因芯片技术可能发生的噪音范围内,该算法对噪音的稳健性优于层次聚类和模糊C均值聚类。将模块聚类算法应用在NCI60数据集上,发现了8个与实验条件高度相关的特征功能模块。

Computational analysis is essential for transforming the masses of microarray datainto a mechanistic understanding of cancer.Here we present a method for findinggene functional modules of cancer from microarray data and have applied it tocolon cancer.First,a colon cancer gene network and a normal colon tissue genenetwork were constructed using correlations between the genes.Then the modulesthat tended to have a homogeneous functional composition were identified by split-ting up...

Computational analysis is essential for transforming the masses of microarray datainto a mechanistic understanding of cancer.Here we present a method for findinggene functional modules of cancer from microarray data and have applied it tocolon cancer.First,a colon cancer gene network and a normal colon tissue genenetwork were constructed using correlations between the genes.Then the modulesthat tended to have a homogeneous functional composition were identified by split-ting up the network.Analysis of both networks revealed that they are scale-free.Comparison of the gene functional modules for colon cancer and normal tissuesshowed that the modules’ functions changed with their structures.

Computational analysis is essential for transforming the masses of microarray datainto a mechanistic understanding of cancer. Here we present a method for findinggene functional modules of cancer from microarray data and have applied it tocolon cancer. First, a colon cancer gene network and a normal colon tissue genenetwork were constructed using correlations between the genes. Then the modulesthat tended to have a homogeneous functional composition were identified by split-ting up the network. Analysis of ...

Computational analysis is essential for transforming the masses of microarray datainto a mechanistic understanding of cancer. Here we present a method for findinggene functional modules of cancer from microarray data and have applied it tocolon cancer. First, a colon cancer gene network and a normal colon tissue genenetwork were constructed using correlations between the genes. Then the modulesthat tended to have a homogeneous functional composition were identified by split-ting up the network. Analysis of both networks revealed that they are scale-free.Comparison of the gene functional modules for colon cancer and normal tissuesshowed that the modules’ functions changed with their structures.

Objective To investigate the effect of ANJIAXIN capsule treatment gene expression profiles and gene functional modules of depression model rats. Methods Gene expression profiles were measured in 8 ANJIAXIN-treated depression model rats and 8 control model rats. Differentially expressed genes were identified and annotated to biological process and cellular component categories of Gene Ontology (GO) database. Then, GO categories enriched with significantly differentially expressed...

Objective To investigate the effect of ANJIAXIN capsule treatment gene expression profiles and gene functional modules of depression model rats. Methods Gene expression profiles were measured in 8 ANJIAXIN-treated depression model rats and 8 control model rats. Differentially expressed genes were identified and annotated to biological process and cellular component categories of Gene Ontology (GO) database. Then, GO categories enriched with significantly differentially expressed genes were identified to investigate the effect of ANJIAXIN capsule treatment at molecular level and gene functional module level. Results Three hundred and thirty differentially expressed genes were identified. Furthermore, eight gene functional modules with significantly gathered differentially expressed genes were recognized, which were mainly related to glycometabolism, protein transport, glutamine metabolism, induction of apoptosis and neurogenesis. Seven differentially expressed genes (Pfkm, Gpx1, Stx1a, Ninj2, Plp, Evl, Nrn1) in the functional modules, which were potentially related to depression, were further identified through literature evidences. Conclusions The effect of ANJIAXIN capsule treatment might be coordinately associated with enhancement of monoamine synthesis and release, and with improvement of neuroprotection and neurogenesis, which suggested the anti-depression effect of ANJIAXIN capsule.

目的研究安佳欣胶囊对抑郁症模型大鼠基因功能类表达的影响。方法分析抑郁症大鼠模型组和安佳欣胶囊组各8例样本的基因表达谱数据,筛选差异表达基因。利用基因功能分类体系Gene Ontology中的生物学过程子树和细胞组分子树,寻找和分析显著聚集差异表达基因的复合功能类,从分子水平和基因功能模块水平探索安佳欣胶囊对抑郁症模型大鼠基因表达谱的影响。结果筛选出330个差异表达基因,并进一步识别了8个差异表达基因功能模块,主要涉及糖代谢、蛋白转运、谷氨酰胺代谢、凋亡诱导和神经发生。通过文献证实进一步发现了差异功能模块中7个可能与抑郁症发病相关的基因(Pfkm、Gpx1、Stx1a、Ninj2、Plp、Evl、Nrn1)在两组动物中差异表达。结论安佳欣胶囊可能增加抑郁症模型大鼠的单胺递质合成和释放,改善神经保护和神经发生功能,这些改变可能与安佳欣胶囊的抗抑郁作用有关。

 
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