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。
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。
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。
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。
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。
A imputation method based on Mahalanobis distance was proposed to estimate missing values in the gene expression data.
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。
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。
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。
Now there are several feature selection methods applied to gene expression data, such as Sequential Forward Selection, GA, S2N and so on.
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。
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。
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。
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。
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。
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。
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.
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。
Expression levels of SF3B1 and SF3B3 and their prognostic value were validated in large cohorts using publicly available gene expression data sets including The Cancer Genome Atlas.
对SF3B1和SF3B3表达水平及其预后价值使用包括癌症基因数据库在内的公开可用的基因表达数据库进行了大样本验证。
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.
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。
Although there has been much work focused on searching gene sets using gene expression data or other prior information, proper statistical testing of the gene sets is still an open question.
尽管目前已经有很多工作利用基因表达谱数据以及一些其他的先验知识来寻找那些与肿瘤相关的基因集合，但还是没有一个恰当的基因集合的统计学方法。
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.
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。
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.
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)。
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。
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。
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