Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

8.5 Target Prediction Ensemble Analysis Results
query were to start from a list containing g = 30 genes and n = 3 prediction
algorithms were selected, then the Cumulative filter would range from one to
30x3 = 90 and setting it on 56 would cause it to report only those microRNAs
that target 56 genes as predicted by any combination of the prediction algo-
rithms (Fig 8.3).
While the Minimum and Cumulative filters operate on microRNAs, the possi-
bility of selecting a subset of the eight prediction algorithms affords the user
control over the number of algorithms required to report a given prediction,
and thus define the criteria by which predictions are deemed accurate. For
example, certain applications - such as experimental validation and cloning of
microRNAs involved in particular pathways of interest - involve a significant
amount of effort to perform. If this is the ultimate goal of the investigation,
then it is desirable to focus on a small set of highly-scoring microRNA targets
unanimously reported by several algorithms. Other searches though, such as
those performed for comparative genomic analyses, are limited only by com-
putational feasibility and can therefore afford to include a greater number of
putative regulators whose involvement may only be predicted by one or two
algorithms.
Any combination of the above filtering methods is possible, keeping in mind
that: (i) they are always applied to the original set of data, and that (ii) a
given gene is included in the final results only if it passes the criteria imposed
by each individual filter set by the user. Therefore, the utility of applying dif-
ferent filter combinations depends on the relevance of the biological question
they help to answer.
8.5 Target Prediction Ensemble Analysis
A question we wanted to ask was whether any prediction algorithm fared bet-
ter than any other combination of prediction algorithms. Since the divergence
between prediction results from any algorithm is large, finding whether any
combination of a subset of these algorithms fares better than any algorithm
alone, is a necessary step. Using our software tool GenemiR we tried to eluci-
date how different combinations of prediction algorithms fared with respect to
the single algorithm, and thus address the hypothesis suggested by Alexiou et
al [16] that combinations of algorithms predict more accurately than the single
algorithm alone. The way we addressed this problem was using relevant ex-
perimental data from exon arrays and microRNA microarrays generated from
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