Most large-scale data computations in the cloud heavily rely on the MapReduce paradigm and on its Hadoop implementation.
We have described the three most large scale protein stability data sets available in human cell lines.
Although there is a variety of parallel programming models, the MapReduce paradigm is considered as the most prominent model for problems of large scale data processing of which the clustering.
MapReduce [1] is considered one of the most prominent programming models for problems of large scale data processing of which the cluster analysis.
Large scale data sharing No. Yes.
Whole genome sequencing has the capability of providing researchers the identity of every variant contained within the genome; however, it may still be too expensive (∼$1,500 2,000 per sample for sequencing only, not including data analysis) for most large-scale studies and may generate more data than required.
(5) The costs of obtaining the consent of millions of patients with health data would be exorbitant and prohibit most large-scale research.
To-date, transcriptional profiling is the most frequently used large-scale data acquisition methodology in clinical research.
Currently, most large-scale TCR-seq uses Illumina sequencers, which can generate extensive sequence data at low cost, although other platforms, in particular 454, have also been used successfully for smaller-scale analyses.
In comparison, the concordance of most large-scale array vs. PCR comparisons [29], [30] is ∼60%, clearly demonstrating the quality of our aCGH data.
In most large-scale lotteries, a very large prize is offered along with many smaller ones.
