A Survey of Support Vector Machines with Uncertainties

Wang, Ximing; Pardalos, Pãnos M.

doi:10.1007/s40745-014-0022-8

Cited by 44 publications

(15 citation statements)

References 63 publications

(74 reference statements)

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“…Support vector machine (SVM) is a popular statistical learning method and has been extensively used to build bioinformatics models [23,36,[41][42][43][44] because of its high efficiency and robust output. In this study, we used the MATLAB function FITCSVM to build our models.…”

Section: Support Vector Machinementioning

confidence: 99%

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

Chen

Xiong

Liu

et al. 2020

Preprint

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Abstract Background: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functionssuch as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA,researcherscanbetter understandthe exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost.However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement.Results: In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVMoffered substantially higher prediction accuracy thanpreviously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.Conclusion: In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species.The result shows that our model outperformed the existing state-of-art models.Our model is available for users through a web serverat http://zhulab.ahu.edu.cn/m5CPred-SVM.

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Section: Support Vector Machinementioning

confidence: 99%

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

Chen

Xiong

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Section: Support Vector Machinementioning

confidence: 99%

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

Chen

Xiong

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA, researchers can better understand the exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost. However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement. Results: In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVM offered substantially higher prediction accuracy than previously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.Conclusion: In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species. The result shows that our model outperformed the existing state-of-art models. Our model is available for users through a web server at http://zhulab.ahu.edu.cn/m5CPred-SVM.

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“…Among various classification algorithms (see Kotsiantis 2007), support vector machine (SVM; Cortes & Vapnik 1995;Vapnik 1998) is one of the most widely used machine learning methods because of its simplicity of use and flexibility with different tasks (Bennett & Campbell, 2000), and has shown excellent performance in many applications (Wang & Pardalos, 2015), such as computer vision (Drucker et al, 1999;Han & Davis, 2012;Mohammed et al, 2011;Osuna et al, 1997;Rosten et al, 2010), bioinformatics (Brown et al, 2000;Che et al, 2011;Inza et al, 2010;Saeys et al 2012;Upstill-Goddard et al, 2013), and fMRI analysis (Poldrack et al, 2009;Serences et al, 2009;Wang et al, 2007), geosciences (Li et al, 2012;Mountrakis et al, 2011;Pradhan, 2013), and finance and business (Huang, 2012;Yang et al, 2011).…”

Section: Support Vector Machinementioning

confidence: 99%

Classifiers as a model-free group comparison test

Kim

Oertzen²

2017

Behav Res

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The conventional statistical methods to detect group differences assume correct model specification, including the origin of difference. Researchers should be able to identify a source of group differences and choose a corresponding method. In this paper, we propose a new approach of group comparison without model specification using classification algorithms in machine learning. In this approach, the classification accuracy is evaluated against a binomial distribution using Independent Validation. As an application example, we examined false-positive errors and statistical power of support vector machines to detect group differences in comparison to conventional statistical tests such as t test, Levene's test, K-S test, Fisher's z-transformation, and MANOVA. The SVMs detected group differences regardless of their origins (mean, variance, distribution shape, and covariance), and showed comparably consistent power across conditions. When a group difference originated from a single source, the statistical power of SVMs was lower than the most appropriate conventional test of the study condition; however, the power of SVMs increased when differences originated from multiple sources. Moreover, SVMs showed substantially improved performance with more variables than with fewer variables. Most importantly, SVMs were applicable to any types of data without sophisticated model specification. This study demonstrates a new application of classification algorithms as an alternative or complement to the conventional group comparison test. With the proposed approach, researchers can test two-sample data even when they are not certain which statistical test to use or when data violates the statistical assumptions of conventional methods.

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A Survey of Support Vector Machines with Uncertainties

Cited by 44 publications

References 63 publications

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

Classifiers as a model-free group comparison test

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