The importance of microarray data analysis lies in presenting functional annotations and classifications.
微阵列数据分析的重要性在于展示功能注释和分类。
Gene selection is a very important problem in microarray data analysis and has critical implications for the discovery of genes related to serious diseases.
基因选择是基因芯片数据分析中的一个重要问题。
The essential and initial problem of gene expression microarray data analysis is to identify differentially expressed genes (DEGs), under certain conditions.
特定条件下的差异表达基因筛选是科学家使用芯片的初衷，也是芯片数据分析中很重要的一种应用。
There is missing value in microarray experiments and it will affect the stability and precision of the expression data analysis.
在基因芯片实验中，数据缺失客观存在，并在一定程度上影响芯片数据后续分析结果的准确性。
In microarray experiments, the missing value does exist and somewhat affect the stability and precision of the expression data analysis.
在基因芯片实验中，数据缺失客观存在，并且在一定程度上会影响芯片数据后续分析结果的准确性。
Missing values contained in microarray data will affect subsequent analysis.
微阵列数据中的缺失值会对随后的数据分析造成影响。
We try to explore the method for explaining the mechanism of Cold - and Hot - ZHENG based on statistical analysis of microarray data.
本文尝试利用大规模基因芯片数据分析对中医寒热证机理进行了初步探索。
In microarray experiments, the missing value does exist and somewhat affects the stability and precision of the expression data analysis.
在不增加实验次数的情况下，缺失值估计是降低缺失数据对后续分析影响的有效方法。
In addition, an analysis object provides methods for data processing, and an image object enables the visualisation of microarray data.
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。
Statistical pattern recognition, with applications to face recognition, data analysis with microarray.
统计模式识别，及在面像识别，基因数据分析中的应用。
These steps are described here and placed in the context of commercial and public tools available for the analysis of microarray data.
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。
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.
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。
The analysis of microarray data requires biologists to collaborate with bioinformaticians or learn the basics of statistics and programming.
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.
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。
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