Multiple Kernel Support Vector Regression for siRNA Efficacy Prediction

The cell defense mechanism of RNA interference has applicationsin gene function analysis and human disease therapy. To effectivelysilence a target gene, it is desirable to select the initiator siRNA moleculeshaving satisfactory silencing capabilities. Computational prediction forsilencing efficacy of siRNAs can assist this screening process before usingthem in biological experiments. String kernel functions, which operatedirectly on the string objects representing siRNAs and target mRNAs,have been applied to support vector regression for the prediction and improvedaccuracy over numerical kernels in multidimensional vector spacesconstructed from descriptors of siRNA design rules. To fully utilize informationprovided by string and numerical kernels, we propose to unifythe two in the kernel feature space by devising a multiple kernel regressionframework where a linear combination of the kernels are used. Weformulate the multiple kernel learning into a quadratically constrainedquadratic programming (QCQP) problem, which although yields globaloptimal solution, is computationally inefficient and requires a commercialsolver package.We further propose three heuristics based on the principleof kernel-target alignment and predictive accuracy. Empirical results onreal biological data demonstrate that multiple kernel regression can improveaccuracy and decrease model complexity by reducing the numberof support vectors. In addition, multiple kernel regression gives insightsinto the kernel combination, which, for siRNA efficacy prediction, evaluatesthe relative significance of the design rules.