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CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

Gillani Z, Akash MS, Rahaman MD, Chen M - BMC Bioinformatics (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China. zeeshan_gillani100@hotmail.com.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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/ .

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Prediction accuracy (AUC) average overall network sizes of unsupervised and supervised methods on knockout, knockdown, multifactorial and average data generated by GeneNetWeaver and extracted fromE. coli. For each network of size: 10, 30, 50, 100, 150, 200 and 500. 10 networks were generated for each size and experimental condition.
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Fig4: Prediction accuracy (AUC) average overall network sizes of unsupervised and supervised methods on knockout, knockdown, multifactorial and average data generated by GeneNetWeaver and extracted fromE. coli. For each network of size: 10, 30, 50, 100, 150, 200 and 500. 10 networks were generated for each size and experimental condition.

Mentions: Gaussian kernel possibly is the best option for prediction of GRN from microarray data as it has high accuracy and less standard derivation on small datasets compared to all other inference methods. It also has overall best performance for all biological conditions (FigureĀ 4).Figure 4


CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

Gillani Z, Akash MS, Rahaman MD, Chen M - BMC Bioinformatics (2014)

Prediction accuracy (AUC) average overall network sizes of unsupervised and supervised methods on knockout, knockdown, multifactorial and average data generated by GeneNetWeaver and extracted fromE. coli. For each network of size: 10, 30, 50, 100, 150, 200 and 500. 10 networks were generated for each size and experimental condition.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4260380&req=5

Fig4: Prediction accuracy (AUC) average overall network sizes of unsupervised and supervised methods on knockout, knockdown, multifactorial and average data generated by GeneNetWeaver and extracted fromE. coli. For each network of size: 10, 30, 50, 100, 150, 200 and 500. 10 networks were generated for each size and experimental condition.
Mentions: Gaussian kernel possibly is the best option for prediction of GRN from microarray data as it has high accuracy and less standard derivation on small datasets compared to all other inference methods. It also has overall best performance for all biological conditions (FigureĀ 4).Figure 4

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China. zeeshan_gillani100@hotmail.com.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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/ .

Show MeSH