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It seems that the mixed network method combines informations of individual datasets together with their noises, which does not improve its performance by integrating multiple datasets.
52– 60 Despite their reported low level of concordance across the different datasets, Hecker et al 13 showed that by integrating multiple datasets, they could reconstruct a majority of the links between the seed network genes simply on the basis of observed correlations between genes in multiple (at least 2 out of 5) gene expression datasets (recreated using BioNetwork Bench, Table 1).
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Two previous algorithms of Deng et al. [ 30] and Ma et al. [ 23] have been proposed to integrating multiple datasets by using the MRF theory for yeast protein function predictions.
If we are integrating multiple datasets, we can search for correlated variables in each dataset.
iASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze ASB.
In order to establish whether integrating multiple datasets can improve prognostic prediction, the six published datasets (described above) were used individually or in combination as 'training sets' for supervised principal components analysis [ 29, 30].
This problem can be greatly reduced by integrating multiple microarray datasets from different studies that can increase the sample size, possibly in the order of thousands.
There are several such expression correlation databases investigating eQTL association patterns within a genetic region of interest available, so the analysis was conducted by GENEVAR, a database and Java tool designed to integrate multiple datasets.
Meta-analysis was adopted to integrate multiple datasets.
Therefore, in this paper, we propose a new model to integrate multiple datasets.
However, not much work has been done to integrate multiple datasets.
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