Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs.

So while there may be significant variance components in siRNA activity associated with the siRNA's target, these appear to have no statistically significant influence on the outcomes of predictive models when removing overlapping siRNAs from training partitions, or at least not specifically to the SVM technique applied to the P+25 feature method.

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The first model M R 1 contained marginally significant evidence for the variance in R being influenced by choice of learning technique alone.

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The second model M R 2 contained evidence for highly significant contribution to the variance in R by choice of feature mapping method alone.

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