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A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction
April-June 2009 (vol. 6 no. 2)
pp. 190-199
ASCII Text
x 
 
Shibin Qiu, Terran Lane, "A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 6, no. 2, pp. 190-199, April-June, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2008.139,
author = {Shibin Qiu and Terran Lane},
title = {A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {6},
number = {2},
issn = {1545-5963},
year = {2009},
pages = {190-199},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.139},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction
IS - 2
SN - 1545-5963
SP190
EP199
EPD - 190-199
A1 - Shibin Qiu,
A1 - Terran Lane,
PY - 2009
KW - Multiple kernel learning
KW - multiple kernel heuristics
KW - support vector regression
KW - QCQP optimization
KW - RNA interference
KW - siRNA efficacy.
VL - 6
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Shibin Qiu, Pathwork Diagnostics, Inc., Sunnyvale
Terran Lane, University of New Mexico, Albuquerque
The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.

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Index Terms:
Multiple kernel learning, multiple kernel heuristics, support vector regression, QCQP optimization, RNA interference, siRNA efficacy.
Citation:
Shibin Qiu, Terran Lane, "A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 6, no. 2, pp. 190-199, April-June 2009, doi:10.1109/TCBB.2008.139
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