Parallel computing in R has been widely used to analyse microarray data. We have seen various applications using various data distribution and calculation approaches. Newer data storage systems, such as MySQL Cluster and HBase, have been proposed for R data storage; while the parallel computation frameworks, including MPI and MapReduce, have been applied to R computation. Thus, it is difficult to understand the whole analysis workflows for which the tool kits are suited for a specific environment. In this paper we propose DSIMBench, a benchmark containing two classic microarray analysis functions with eight different parallel R workflows, and evaluate the benchmark in the IC Cloud testbed platform
We have implemented a self-contained package for DNA microarray analysis in R. The package is named ...
The high volume and complexity of microarray data has created both opportunities and challenges for ...
Abstract Background With the advent of high throughput genomics and high-resolution imaging techniqu...
Parallel computing in R has been widely used to analyse microarray data. We have seen various applic...
Abstract Background Microarray analysis allows the simultaneous measurement of thousands to millions...
The statistical language R is favoured by many biostatisticians for processing microarray data. In r...
Nowadays, microarray technology has become one of the popular ways to study gene expression and diag...
Background: R is the preferred tool for statistical analysis of many bioinformaticians due in part t...
Abstract Background R is the preferred tool for statistical analysis of many bioinformaticians due i...
The statistical language R is favoured by many biostatisticians for processing microarray data. In r...
Motivation: High performance computing (HPC) clusters play a pivotal role in large-scale bioinformat...
Currently, clustering applications use classical methods to partition a set of data (or objects) in ...
Microarray technology is still an important way to assess gene expression in molecular biology. This...
Abstract: Background: Pre-processing, including normalization of raw microarray data is crucial to m...
Abstract. Studies of gene expression using high-density oligonucleotide microar-rays have become sta...
We have implemented a self-contained package for DNA microarray analysis in R. The package is named ...
The high volume and complexity of microarray data has created both opportunities and challenges for ...
Abstract Background With the advent of high throughput genomics and high-resolution imaging techniqu...
Parallel computing in R has been widely used to analyse microarray data. We have seen various applic...
Abstract Background Microarray analysis allows the simultaneous measurement of thousands to millions...
The statistical language R is favoured by many biostatisticians for processing microarray data. In r...
Nowadays, microarray technology has become one of the popular ways to study gene expression and diag...
Background: R is the preferred tool for statistical analysis of many bioinformaticians due in part t...
Abstract Background R is the preferred tool for statistical analysis of many bioinformaticians due i...
The statistical language R is favoured by many biostatisticians for processing microarray data. In r...
Motivation: High performance computing (HPC) clusters play a pivotal role in large-scale bioinformat...
Currently, clustering applications use classical methods to partition a set of data (or objects) in ...
Microarray technology is still an important way to assess gene expression in molecular biology. This...
Abstract: Background: Pre-processing, including normalization of raw microarray data is crucial to m...
Abstract. Studies of gene expression using high-density oligonucleotide microar-rays have become sta...
We have implemented a self-contained package for DNA microarray analysis in R. The package is named ...
The high volume and complexity of microarray data has created both opportunities and challenges for ...
Abstract Background With the advent of high throughput genomics and high-resolution imaging techniqu...