The traditional clustering techniques are not suitable to be directly applied to gene expression time series data, because of the inhered properties of local regulation and time shift. In order to cope with the existing problems, the local similarity and time shift, we have developed a new ...
Gene expression data clustering based on particle pair and differential evolution基于粒子对和差分进化的基因表达数据聚类基因聚类K-means算法粒子对差分进化混合算法In order to solve the problem that particle pair algorithm existed local optimization premature to lower precision and the clustering results were ...
Clustering technology, as one of the important tools of analyzing gene expression data and identifying gene function, has been used widely. In this paper we discuss main clustering technology about gene expression data at present, analyze their advantages and disadvantages, present the methods to ...
Clustering, which has been widely used as a forecasting tool for gene expression data, remains problematic at a very deep level: different initial points of clustering lead to different processes of convergence. However, the setting of initial points is
Clustering is crucial for gene expression data analysis. As an unsupervised exploratory procedure its results can help researchers to gain insights and formulate new hypothesis about biological data from microarrays. Given different settings of microarra
基因表达谱/GeneExpressionData| 固定链接 发布人:liupq 基因表达谱聚类分析(5) k-平均聚类(k-means algorithms) 3月 10, 2008 Thek-means algorithm is one of a group of algorithms calledpartitioning methods. The problem of partitional clustering can be formally stated as follows: Givennobjects in ad...
A key property of this representation is that each cluster of the expression data corresponds to one sub tree of the Minimum Spanning Tree, which converts a multidimensional clustering problem to a tree partitioning problem. Each node represents one gene, and every edge is associated with a ...
p pBackground/p pTranscript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST digital northern, are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties...
However, the computational limitation to achieve this precise target of tight clusters prohibits it from being used for large microarray gene expression data or any other large data set, which are common nowadays. We propose a pragmatic and scalable version of the tight clustering method that is ...
In addition, the cell-type-specific gene expression matrix was estimated by clustering the combined 27 single-cell data sets. Marker genes were then chosen to match the genes used in the immune-cell-specific signature from CIBERSORT9, and expression values for each marker gene were averaged ...