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For the end user, the ArrayExpress package eliminates, or at least greatly reduces the amount of manual intervention needed and helps towards automated processing of large collections of datasets.
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The next section explores this application on a large collection of datasets.
Applying such methods across a large collection of datasets is impeded by the dataset, platform and technology biases (Leek et al., 2010; Rung and Brazma, 2013).
However, for a large collection of dataset images, the deep architecture can only be efficiently trained using powerful graphics processing units (GPUs).
The availability of large collections of microarray datasets (compendia), or knowledge about grouping of genes into pathways (gene sets), is typically not exploited when training predictors of disease outcome.
We apply CVIIM to large collections of microarray datasets to answer this question for normalization and PCA.
Note that this decrease is simply a consequence of requiring agreement among a larger collection of datasets.
Two procedures for partitioning large collections of highly intermixed datasets of different classes into a number of hyper-spherical or hyper-ellipsoidal clusters are presented.
Specialized resources exist to enable very high-throughput computing for specific applications (Pekurovsky et al., 2004; Swindells et al., 2002), but few computational options exist allowing researchers to quickly mine large collections of genome-scale datasets.
A large collection of regression datasets was used to assess the performance of each method (Table 1).
Hence, a database with a large collection of curated datasets will be enormously valuable for the community.
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