DYNAMIC CLUSTERING OF CELL-CYCLE MICROARRAY DATA

Correcting the loss of cell-cycle synchrony in clustering analysis of microarray data using weights

MOTIVATION Due to the existence of the loss of synchrony in cell-cycle data sets, standard clustering methods (e.g. k-means), which group open reading frames (ORFs) based on similar expression levels, are deficient unless the temporal pattern of the expression levels of the ORFs is taken into account. METHODS We propose to improve the performance of the k-means method by assigning a decreasin...

Clustering Microarray Data

We begin with an example that will be used throughout the chapter.The data come from Sorlie et al. (2001). The goal of that article was to “classify breast carcinomas based on variations in gene expression derived from complementary deoxyribonucleic acid (cDNA) microarrays and to correlate tumor characteristics to clinical outcome.’’ The data consist of log fluorescence values for 456 cDNA clon...

Construction and Clarification of Dynamic Gene Regulatory Network of Cancer Cell Cycle via Microarray Data

BACKGROUND Cell cycle is an important clue to unravel the mechanism of cancer cells. Recently, expression profiles of cDNA microarray data of Cancer cell cycle are available for the information of dynamic interactions among Cancer cell cycle related genes. Therefore, it is more appealing to construct a dynamic model for gene regulatory network of Cancer cell cycle to gain more insight into the ...

Clustering Microarray Data

Using microarray data, which gives thousands of genes’ expression levels at once, we examine different clustering techniques and evaluation methods to effectively cluster the noisy data. The results of this study has implications for the field of biology. Genes with similar functions are grouped together, which gives insight into specific genes and their role in the cell. The cluster analysis e...

cDNA Microarray Data Normalization