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The Relative Gain Array pairing technique is used to define the adaptive predictive control strategy, i.e. the AP models structure.
The resulting, smoothed threshold fold-difference envelope was then applied to the two compound-treated/control array pairs to identify those probe-sets that have large expression level differences between treatment and control.
The experimental design for array pairing was as follows (Fig. 2).
We use this metric to rank order all array pairs between the two species being analyzed.
For these two non-linear array pairs, the two members of a pair had no significant sequence similarity.
In this article, we extend the Le et al. method so that we can integrate expression values and text when searching for matched array pairs.
As before, for each of the human genes we selected the top 10 mouse genes with the greatest Spearman's correlation in the matched array pairs.
For each, we used the set of matched array pairs to select the 10 mouse genes with the greatest Spearman's correlation resulting in 435 total genes.
Since we expect the vast majority of random array pairs to represent different experiments, as negative training data we used all pairs not in the positive training set (initially 11 575 802 pairs).
To test the usefulness of our new method for determining the function of genes, we used our set of matched array pairs between human and mouse to identify mouse cell cycle genes.
At this point we perform the co-training step: we take all array pairs in the intersection of the top 1% of expression similarity scores and the top 1% of textual similarity scores and add them to the initial positive training set.
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