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。
A systematic comparison and evaluation of biclustering methods for gene expression data.
〉基因表达数据的并行双向聚类算法。
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。
The proposed algorithm include three steps: firstly, the pretreatment to the gene expression data;
该算法主要包括三个步骤：首先对数据进行预处理；
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。
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。
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。
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 are many clustering algorithms have been applied to gene expression data now, and new algorithms are proposed continuously.
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。
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。
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。
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。
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。
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。
"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.
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。”
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。
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。
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。
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