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All our experiments and developments use the libSVM package (6).
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To implement the SVR, we used the LibSVM package [38] with the radial basis function kernel, whose parameters were estimated by cross-validation during the training session.
As described in the Methods section, we used the libsvm package to train a support vector machine with features of the mRNA: total length, 3'UTR length and G+C content.
> -wrap-foot> Two support vector machines (SVMs) were trained with linear kernel functions, using the libsvm package (http://www.csie.ntu.edu.tw/~cjlin/libsvm).ntu.edu.tw/~cjlin/libsvm
The method was implemented in Python using the LIBSVM package (http://www.csie.ntu.edu.tw/~cjlin/libsvm).ntu.edu.tw/~cjlin/libsvm
We used the LIBSVM package developed by Chih-Chung Chavailablehih-Jen Lin (atailable at http://www.csie.ntu.edu.tw/∼cjlin/libsvm/) to construct SVMs for every exemplar 8mer.
The SVR part is accomplished using the LIBSVM software package [34], in which ε-SVR is adopted to perform the regression analysis.
We performed SVM classification using the LibSVM software [ 41].
We used SVM implemented in the LIBSVM package (Chang and Lin, (2003) with the linear kernel.
The simulations for the SVM are carried out using the popular LIBSVM package in C [35].
For the SVM classifier, we have used the nu-SVC classifier from the libsvm package in WEKA.
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