Background: Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large d...
Geraint Barton, J. C. Abbott, Norie Chiba, D. W. H...
Background: The identification of transcription factors (TFs) associated with a biological process is fundamental to understanding its regulatory mechanisms. From microarray data,...
Background: In many research areas it is necessary to find differences between treatment groups with several variables. For example, studies of microarray data seek to find a sign...
Background: The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. Wh...
Junhee Seok, Amit Kaushal, Ronald W. Davis, Wenzho...
This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polis...
Background: Microarray technology is a high-throughput method for measuring the expression levels of thousand of genes simultaneously. The observed intensities combine a non-speci...
Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineeri...
Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in m...
Background: Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the under...
Page 1 of 2Exploiting sample variability to enhance multivariate analysis of microarray data -- Möller-Le... http://bioinformatics.oxfordjournals.org/cgi/content/abstract/23/20/2...