Gillani Zeeshan, Akash Muhammad Sajid Hamid, Rahaman M D Matiur, Chen Ming
Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
Institute of Pharmacology, Toxicology and Biochemical Pharmaceutics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
BMC Bioinformatics. 2014 Nov 30;15(1):395. doi: 10.1186/s12859-014-0395-x.
Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.
We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network.
For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .
从表达数据预测基因调控网络（GRN）是一项具有挑战性的任务。已经开发了许多方法来应对这一挑战，从监督方法到无监督方法。最有前景的方法是基于支持向量机（SVM）。需要对监督方法SVM在不同生物学实验条件和网络规模下使用不同核的预测准确性进行全面分析。
我们开发了一种基于SVM的工具（CompareSVM），用于比较不同核方法对GRN的推断。使用CompareSVM，我们详细研究和评估了不同SVM核方法在不同大小的微阵列模拟数据集上的性能。从CompareSVM获得的结果表明，推断方法的准确性取决于实验条件的性质和网络的规模。
对于节点数小于200且平均（在所有网络规模上）的网络，与所有其他推断方法相比，SVM高斯核在基因敲除、基因敲低和多因素数据集中表现更优。对于具有大量节点（约500个）的网络，推断方法的选择取决于实验条件的性质。CompareSVM可在http://bis.zju.edu.cn/CompareSVM/获取。
