Without the atlas each researcher could spend a lifetime trying to gather complete gene-expression data for his or her work.
因为如果没有图谱，每个研究者为了他或他的研究，可能会花费一生的时间来搜集完整的基因表达数据。
This paper proposed a new non-parametric algorithm for clustering gene expression data.
提出了一种用于基因表达数据的无参数聚类算法。
The theory and method of neural network ensemble were studied in the given gene expression data.
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。
Through seriate genome-wide mRNA expression data, similarity between two genes could be measured.
通过分析酵母细胞的大规模基因表达谱数据，获得不同基因间表达的相关性。
Objective Discuss the condition and the effect of SVM in the classification of gene expression data.
目的探讨支持向量机在基因表达数据分类研究中的应用条件和效果。
There is some obvious inaccuracy of gene expression in the experiment to obtain the gene expression data.
在基因表达谱数据获取过程中，基因表达谱数据含有较大的实验误差。
The list of genes was shifted with respect to the expression data, so that the one did not correspond with the other.
表中基因的一列相对于表达数据发生了调换，导致两项不相对应。
In this chapter we discuss one of the few abundant sources for temporal information, time series expression data.
在这一章中，我们讨论了时间信息的一些丰富来源中的一种，时间序列表达数据。
To search a new and effective method for feature extraction and classification based on microarray expression data.
基于微阵列表达数据，探索新的有效特征提取和分类方法。
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。
With the extensive applications of DNA microarray technology, huge amounts of gene expression data have been generated.
随着基因芯片技术的广泛应用，产生了海量的基因表达数据。
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。
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。
Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks.
最后，我们提出了结合时间序列表达数据和静态数据来构建动态调控网络的方法。
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。
According to the characteristics of gene expression data, a high accurate density-based clustering algorithm called DENGENE was proposed.
根据基因表达数据的特点，提出一种高精度的基于密度的聚类算法DENGENE。
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。
This thesis improves classification using gene expression data method in two aspects: feature selection and SVMs classification algorithm.
针对基于基因表达数据的分类，本文从特征基因选择和支持向量机分类算法两个方面进行了改进。
Another alleged error the researchers at the Anderson centre discovered was a mismatch in a table that compared genes to gene-expression data.
另一个据Anderson中心的研究员指出的错误是一张表中基因和其基因表达数据的不匹配。
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。
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 thesis improves tumor samples classification of gene expression data in two aspects: classification algorithm and feature gene selection method.
针对基于基因表达数据的肿瘤样本分类，本文从分类算法和特征基因选取方法两个方面进行了改进。
The problem of feature gene selection and tumor samples classification of microarray gene expression data is one of challenges of gene microarray technology.
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。
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。
Experiments prove that the method is valid and its performance is higher than the other imputation methods based on k-nearest neighbors for gene expression data.
实验结果证明了该算法具有有效性，其性能优于其他基于最近邻居法的缺失值处理算法。
Similarly, in publicly available breast cancer gene expression data sets, overexpression of SF3B3, but not SF3B1, was significantly correlated with overall survival.
同样，在公开的乳腺癌基因表达数据库中，SF3B 3过表达与总生存率显著相关，SF 3b1则无此作用。
As the gene expression data owns the characteristics of nonlinear and high noise, normal Euclidean distance can not represent the similarity measurement between genes.
由于基因表达谱非线性的特点，普通的欧几里得距离无法很好地表示基因之间的相似性度量。
As the gene expression data owns the characteristics of nonlinear and high noise, normal Euclidean distance can not represent the similarity measurement between genes.
由于基因表达谱非线性的特点，普通的欧几里得距离无法很好地表示基因之间的相似性度量。
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