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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction
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A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction

机译：多核支持向量回归的框架及其在siRNA功效预测中的应用

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摘要

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.
机译：RNA干扰的细胞防御机制已应用于基因功能分析，并有望在人类疾病治疗中发挥潜力。为了有效地使靶基因沉默，期望选择具有令人满意的沉默能力的合适的引发剂siRNA分子。 siRNA沉默功效的计算预测可在将其用于生物学实验之前协助此筛选过程。直接在代表siRNA和目标mRNA的字符串对象上运行的字符串核函数已被用于支持向量回归，以预测和提高由siRNA设计规则的描述符构造的多维向量空间中数字核的准确性。为了充分利用字符串和数字数据提供的信息，我们建议通过设计使用核的线性组合的多核回归框架，在核特征空间中将两者统一起来。我们将多核学习公式化为二次约束二次规划（QCQP）问题，该问题尽管产生全局最优解，但对计算的要求很高，并且需要商业求解程序包。我们进一步提出了基于核目标对齐和预测精度的三种启发式算法。实证结果表明，多核回归可以通过减少支持向量的数量来提高准确性，降低模型复杂性并显着加快计算性能。此外，多核回归评估了组成核的重要性，对于siRNA功效预测问题，它比较了设计规则的相对重要性。最后，我们深入分析了多核回归机制并指出了可能的扩展。

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