Background: Serial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Bin...
Background: Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the explorati...
Xuejun Liu, Kevin K. Lin, Bogi Andersen, Magnus Ra...
Background: Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, a...
Background: High-throughput microarrays are widely used to study gene expression across tissues and developmental stages. Analysis of gene expression data is challenging in these ...
Terri T. Ni, William J. Lemon, Yu Shyr, Tao P. Zho...
Background: High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade....
Federico M. Giorgi, Anthony M. Bolger, Marc Lohse,...
Background: Experimental studies of gene expression have identified some of the individual molecular components and elementary reactions that comprise and control cellular behavio...
Background: Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is crit...
Kyungpil Kim, Shibo Zhang, Keni Jiang, Li Cai, In-...
Background: The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compa...
Background: Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariat...
Background: Gene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good...