分析、显示、编辑重测序微阵列数据软件。
ResqMi is a new tool for analyzing, viualizing and editing Resequencing Microarray data.
微阵列数据分析的重要性在于展示功能注释和分类。
The importance of microarray data analysis lies in presenting functional annotations and classifications.
微阵列数据中的缺失值会对随后的数据分析造成影响。
Missing values contained in microarray data will affect subsequent analysis.
文中介绍了基因微阵列数据的聚类分析方法及其重要应用。
This paper presents a system of clustering analysis for DNA microarray dat…
高吞吐量的方法增加了几个数量级的可用实验的微阵列数据的量。
High throughput methodologies have increased by several orders of magnitude the amount of experimental microarray data available.
这里描述了这些步骤，并将它们置于商用和公共可用的微阵列数据分析环境中。
These steps are described here and placed in the context of commercial and public tools available for the analysis of microarray data.
在三个公开的基因微阵列数据集上进行了实验，提出的算法能够得到更佳的分类性能。
Experimental results with the three public datasets demonstrate that the proposed algorithms can obtain the better performance.
关联分析方法用于分析微阵列数据集基因间相关联系，生成关联规则，进而构建基因调控网络。
The association analysis method can be applied to analyzing the microarray datasets, mining association rules of genes, and then constructing gene regulatory network.
背景：当处理微阵列数据集时，我们最近注意到一些基因名字正在被不经意地转变为非基因名字。
BACKGROUND: When processing microarray data sets, we recently noticed that some gene names were being changed inadvertently to non-gene names.
用于管理和分享微阵列数据的通用标准和本体论的采用是必要的，并将对研究社区提供直接的益处。
The adoption of common standards and ontologies for the management and sharing of microarray data is essential and will provide immediate benefit to the research community.
微阵列非常适合于整合的系统生物学方式，但是没有一个现有的微阵列数据库是集中于拷贝数变化的。
Microarrays are well suited for the integrative systems biology approach, but none of the existing microarray databases is focusing on copy number changes.
此外，一个分析对象提供了用于数据处理的方法，及一个图形对象使得能够进行微阵列数据的可视化。
In addition, an analysis object provides methods for data processing, and an image object enables the visualisation of microarray data.
微阵列数据的分析需要生物学家 与生物信息学家或学习统计的基础知识和编程。
The analysis of microarray data requires biologists to collaborate with bioinformaticians or learn the basics of statistics and programming.
有两个例子是我们的研究，如DNA微阵列数据使用DNA微阵列数据的模糊art模糊神经网络和基因聚类的癌症患者预后的预测。
Two examples are our research such as the prediction of prognosis for cancer patients from DNA microarray data using FNN and gene clustering for DNA microarray data using fuzzy ART.
结论：通过提供数据交换的一种常见平台，MAGE将帮助微阵列数据产生者和用户交换信息，并且MAGE - STK将使得MAGE的采用变得更加容易。
CONCLUSIONS: MAGE will help microarray data producers and users to exchange information by providing a common platform for data exchange, and MAGE-STK will make the adoption of MAGE easier.
给出一个微阵列的数据组，标示出测定中的错误的所有来源，并在特定资料组中找出它们。
Given a microarray data set, articulate all the sources of error in measurement and find them in the particular data set.
实验数据包含S在上述的条件下以微阵列分析测量的转录体特征。
The experimental data consists of transcriptome profiles of s under the conditions described above measured by micro array.
基于微阵列表达数据，探索新的有效特征提取和分类方法。
To search a new and effective method for feature extraction and classification based on microarray expression data.
目的探讨从巨量的微阵列基因数据中挖掘肿瘤相关分子机理及功能信息；
Objective To explore the tumor related molecular mechanism and functional message from the gigantic cDNA array gene data.
利用ICA对基因微阵列表达谱数据进行分解获得由基因模型谱和对应系数构成的线性谱模型，并在此基础上进行基因分类。
The gene linear profile model, composed of model profiles and coefficients, is obtained by ica from gene expression data, so gene classification based on ica is presented.
以一个典型的微阵列基因表达数据集为背景研究了神经网络集成的理论和方法。
The theory and method of neural network ensemble were studied in the given gene expression data.
从微阵列试验中获得的大量数据不利于传统的分析方法。
The copious data arising from microarray experiments is not conducive to traditional analytical approaches.
这是由于它们处理高维复杂数据集的能力，例如那些由蛋白质质谱和DNA微阵列实验产生的。
This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments.
目的基于微阵列表达数据，探索筛选差异表达基因的有效方法。
Objective To search an effective method on screening significant genes based on microarray data.
在本研究中，我们力图比较微阵列分析中的多种数据库的效用。
In this study we sought to compare the utility of various databases in microarray analysis.
因而，微阵列实验的范围和复杂性需要计算机软件程序做很多数据处理，存储，可视化，分析和转换工作。
As a consequence, the scale and complexity of microarray experiments require that computer software programs do much of the data processing, storage, visualization, analysis and transfer.
本发明提供具有明亮的基准标记的微阵列及从其获得光学数据的方法。
The invention provides a microarray having a bright fiducial mark and a method of obtaining a light data from the same.
基因表达数据的特征基因选取和肿瘤样本分类问题是基因微阵列技术的挑战性课题之一。
The problem of feature gene selection and tumor samples classification of microarray gene expression data is one of challenges of gene microarray technology.
介绍了目前几种基于DNA微阵列基因表达数据的分类方法。
Several classification methods based on DNA gene expression microarray data are introduced in this paper.
尽管很多有意义的结果已经从微阵列研究中获得，一个局限是展示和交换这些数据的标准的缺乏。
Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data.
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