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。
Without the atlas each researcher could spend a lifetime trying to gather complete gene-expression data for his or her work.
〉基因表达数据的并行双向聚类算法。
A systematic comparison and evaluation of biclustering methods for gene expression data.
提出了一种用于基因表达数据的无参数聚类算法。
This paper proposed a new non-parametric algorithm for clustering gene expression data.
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。
With the extensive applications of DNA microarray technology, huge amounts of gene expression data have been generated.
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.
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。
Objective Discuss the condition and the effect of SVM in the classification 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。
Currently, cluster methods are used most frequently among the methods applied to the analysis of gene expression data.
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。
One model is fuzzy cluster analysis of gene expression data based on a cluster validity measure named Xie-Beni index.
提出了一种基于马氏距离的填充算法来估计基因表达数据集中的缺失数据。
A imputation method based on Mahalanobis distance was proposed to estimate missing values in the 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。
The theory and method of neural network ensemble were studied in the given gene expression data.
现在已有不少的算法开始应用于基因表达数据分析，而且不断有新的算法提出。
There are many clustering algorithms have been applied to gene expression data now, and new algorithms are proposed continuously.
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。
Along with the research and extensive application of DNA chip technology, gene expression data analysis have become a hotspot in life science field.
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。
According to the characteristics of gene expression data, a high accurate density-based clustering algorithm called DENGENE was proposed.
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。
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.
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.
泽滋在大学发表一则新闻中解释道：“仔细阅读反应，应用基因表达数据揭示与人起反应的病原型。”
"A detailed reading of that response, using gene expression data, reveals what type of pathogen the person is reacting to, " Zaas explained in a news release from the university.
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。
Through support vector machine algorithms for gene expression data classification training, SVMs provide a effective way for analysis of gene expression data.
针对基因表达数据含有噪声的特点，提出了基于总体最小二乘估计的基因表达缺失值估计算法。
Consider the additive noise in the expression dataset, a new method based on Total Least Squares (TLS) is presented.
目前已有不少特征选取方法应用于基因表达数据，比如顺序前进法、遗传算法、信噪比指标等。
Now there are several feature selection methods applied to gene expression data, such as Sequential Forward Selection, GA, S2N and so on.
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。
Another alleged error the researchers at the Anderson centre discovered was a mismatch in a table that compared genes to gene-expression data.
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。
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。
This thesis improves tumor samples classification of gene expression data in two aspects: classification algorithm and feature gene selection method.
方法以基因表达数据矩阵为出发点，用相关系数描述基因之间的相互关系，并在某一阈值下，建立起基因调控网络图。
Methods Based on gene expression matrix, correlation number is used to describe the relationships between genes. At some threshold, the gene network can be established.
这样做的结果是大大节省了时间。因为没有图谱，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。
The result is a massive saving in time, since without the atlas each researcher could spend a lifetime trying to gather complete gene-expression data for his or her work.
本研究基于自组织映射网络(SOM)，分析多骨髓瘤基因表达数据，建立预测多骨髓瘤的自组织预测模型(SOPM)。
Multiple myeloma gene expression data was analyzed and Self Organization Prediction Model (SOPM) based on Self-Organization Mapping (SOM) networks was established for predicting multiple myeloma.
在这个研究领域里，基于基因表达数据的样本分类扮演着很重要的角色，它一般具有两个关键步骤：基因选择和分类模型设计。
In these research areas, sample classification based on gene expression data is acting a very important role, it generally has two pivotal steps: gene selection and Classifier design.
利用随机矩阵理论(RMT)方法除去肺癌基因表达数据中的噪声，并将去噪后的数据分别用模块方法和等级聚类方法进行处理。
We used random matrix theory (RMT) to remove the noises in lung cancer gene expression data and used the modules approach and the hierarchical clustering approach to construct the gene networks.
方法通过实际基因表达数据考核其应用效果，并通过模拟试验进一步验证和研究在存在大量无差异表达基因情况下对分类产生的影响。
Methods the method is applied to real datasets. The result of simulated experiment validation shows the effect of classification with many undifferentiated expressed genes.
方法通过实际基因表达数据考核其应用效果，并通过模拟试验进一步验证和研究在存在大量无差异表达基因情况下对分类产生的影响。
Methods the method is applied to real datasets. The result of simulated experiment validation shows the effect of classification with many undifferentiated expressed genes.
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