The analysis and research on gene expression data is an important research area of bioinformatics.
基因表达数据的分析和研究是生物信息学中重要的研究课题。
The cluster analysis of gene expression data is an important means for discovering gene functions and regular to mechanisms.
基因表达谱数据的聚类分析对于研究基因功能和基因调控机制有重要意义。
The cluster analysis of gene expression data is an important means for discovering gene functions and regulatory mechanisms.
基因表达谱数据的聚类分析对于研究基因功能和基因调控机制有重要意义。
Along with the research and extensive application of DNA chip technology, gene expression data analysis have become a hotspot in life science field.
随着DNA芯片技术的广泛应用，基因表达数据分析已成为生命科学的研究热点。
Through support vector machine algorithms for gene expression data classification training, SVMs provide a effective way for analysis of gene expression data.
通过支持向量机训练算法对基因表达数据进行分类训练，为分析基因数据提供有效的手段。
MOTIVATION: Gene expression analysis with microarrays has become one of the most widely used high-throughput methods for gathering genome-wide functional data.
动机：用微阵列进行基因表达分析已经成为收集全基因组功能数据的最广泛使用的高通量方法之一。
There are lots of cluster methods applied to the analysis of gene expression data.
常用于基因表达数据分析的聚类方法有很多。
One model is fuzzy cluster analysis of gene expression data based on a cluster validity measure named Xie-Beni index.
一种模型是基于有效性测度谢白尼指数的基因表达数据的模糊聚类分析。
Currently, cluster methods are used most frequently among the methods applied to the analysis of gene expression data.
目前对基因表达数据进行分析的各种方法中，聚类分析方法应用得最多。
Then, the housekeeping gene was used to adjust the rest gene expression data in order to keep the correct rate of pre-analysis gene expression data.
再利用看家基因调整余下的基因表达数据，从而保证待分析的基因表达数据的正确率。
This paper focuses mainly on investigating and studying clustering analysis problems of high directional dimensional data , which includes gene expression data and text data .
本文针对高维数据的方向性及其聚类分析中出现的问题进行了研究。
Bioinformatic data of High Throughout Genomic Sequences (HTGS) and Serial analysis of Gene expression (SAGE) were used for analysis of chromosome localization and tissue expression.
以高通量基因组序列(HTGS)数据库及SAGE文库为基础，进行染色体定位及组织表达分析。
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。
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。
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