CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks
CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks
复制标题
CompareSVM：基因规律性网络的监督式支持向量机 (SVM) 推理
DOI：
10.1186/s12859-014-0395-x
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发表时间：
2014-11-30
影响因子：
3
通讯作者：
Chen, Ming
中科院分区：
文献类型：
--
作者：
Gillani, Zeeshan;Akash, Muhammad Sajid Hamid;Chen, Ming
背景从基因表达数据中预测基因规则性网络（GRN）是一个具有挑战性的任务.已经开发了许多方法来解决这一挑战，从监督到无监督的方法。最有前途的方法是基于支持向量机（SVM）。有必要对不同的生物实验条件和网络size.ResultsWe开发了一个工具（CompareSVM）的基础上支持向量机来比较不同的内核方法的推理GRN的监督方法SVM的预测精度进行全面的分析。使用CompareSVM，我们详细研究和评估了不同的SVM核方法在不同大小的芯片模拟数据集。从CompareSVM得到的结果表明，推理方法的准确性取决于实验条件和大小的network.ConclusionsFor网络节点（<200）和平均（在所有大小的网络）的性质，SVM高斯核优于敲除，击倒，和多因子数据集相比，所有其他推理方法。对于具有大量节点（~500）的网络，推理方法的选择取决于实验条件的性质。CompareSVM可在 http://bis.zju.edu.cn/CompareSVM/ .
BackgroundPredication 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.ResultsWe 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.ConclusionsFor 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/ .