The statistical language R is favoured by many biostatisticians for processing microarray data. In recent times, the quantity of data that can be obtained in experiments has risen significantly, making previously fast analyses time consuming or even not possible at all with the existing software infrastructure. High performance computing (HPC) systems offer a solution to these problems but at the expense of increased complexity for the end user. The Simple Parallel R Interface is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop-in parallelised replacements of existing R functions. In this paper we describe parallel implementations of two popular techniques: exploratory clustering analyses using the random forest classifier and feature selection through identification of differentially expressed genes using the rank product method.

The statistical language R and its Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by some analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing systems offer a solution to this issue. The Simple Parallel R Interface (SPRINT) is a package that provides biostatisticians with easy access to High Performance Computing systems and allows the addition of parallelized functions to R. Previous work has established that the SPRINT implementation of an R permutation testing function has close to optimal scaling on up to 512 processors on a supercomputer. Access to supercomputers, however, is not always possible, and so the work presented here compares the performance of the SPRINT implementation on a supercomputer with benchmarks on a range of platforms including cloud resources and a common desktop machine with multiprocessing capabilities. Copyright © 2011 John Wiley & Sons, Ltd.

The statistical language R and Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by these analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing (HPC) systems offer a solution to this issue. The Simple Parallel R INTerface (SPRINT) is a package that provides biostatisticians with easy access to HPC systems and allows the addition of parallelized functions to R. This paper will present how we added a parallelized permutation testing function in R using SPRINT and how this function performs on a supercomputer for executions of up to 512 processes.

Nowadays, we can observe the transition of traditional business to electronic one, where negotiations still play very important role. In the paper we propose a general model of human negotiations and on the basis of which we have created a simplified model of quality analysis. The proposed quality model allows us to define various negotiation scenarios and to evaluate such negotiation metrics as completeness, effectiveness, performance and satisfaction. Two kinds of negotiations (f2f and chat) have been compared for buying/selling scenarios. More than 150 experiments have been carried out taking into account negotiation quality. Firstly, analysis has been made to examine the impact of negotiator personalities, experiences, roles and positions on that quality. Secondly, further studies have shown how negotiation quality is influenced by various types of negotiation strategies.