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To investigate the integrity of the reference database and the concept in general, we generated a rank-based signature from an independent expression profiling experiment conducted by Lee et al. [ 42].
We then generated a ranking of genes based on multifunctionality, using PPV instead of AUC as the optimization criterion (see Methods).
In addition, modelMaGe automatically generated a ranking of the fitted models according to the Akaike Information Criterion corrected for small sample size (AICc).
By repeating the described procedure for all samples and counting the number of times a gene was picked out, we generated a ranked list with the most frequently identified genes and evaluated the classification error.
Using the data from the deletion- and overexpression-based screens, we generated a ranked list of sensitive and resistant strains and performed epistasis analysis by systematically creating double mutants between genes involved in CS resistance and measuring their fitness in the presence of glyoxal using high resolution growth curve analysis on the approximately 800 multimutants.
By hunting in the module of interest for transcriptional regulators (DNA binding transcription factors and co-factors), or asking the related question "which transcriptional regulator has the highest absolute, average correlation to all the genes in the module?", we generated a ranked list of regulators predicted to control the processes in question.
Our calculation on the Dscam1 isoform population generated a ranking of contributions of the different exons to self-binding affinity: exon 4 > exon 6 > exon 9. Therefore, the model predicted the order of self-binding affinity of the above case as x.27.25 > 7.x.25 > 7.27.x, matching well with previously published biochemical studies [ 19].
In the rest of this section, we will provide some examples to show that the SDR method does not always generate a rank one solution.
In these scenarios, we believe that SNAP can generate a rank order of mutations for further study.
Thus, for each disease, we used the meta-analysis approach to generate a rank ordered list of up- and down-regulated genes relative to controls.
3. Generate a rank matrix { R mn, 1 ≤ m, n ≤ N} by converting the distances to ranks such that the row m in the rank matrix is the order statistic of the row m of the distance matrix.
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