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From this, we aim at identifying a time scale for which networks constructed from normal data are different to networks from randomized data, with respect to measures we are using in this paper.
Normal data are described as arithmetic mean ± SD.
The plots of the frequency distributions of the log normal data are shown here [see Additional file 10].
Clinical importance of the method is providing pathway interpretation of single cancer, even though its matched normal data are unavailable.
Moreover, Cole [ 34] shows that mean and SD for normal data are commonly used for the construction of curves as well as the LMS method for the treatment of asymmetry.
Therefore, we suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patient's matched normal data are unavailable.
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Continuous normal data were compared using Student's t-test or analysis of variance.
1-class SVM is a special variant of the general support vector machine (SVM) and since only the normal data is required for training, 1-class SVM is widely used in anomaly detection.
We show that the traditional approach of choosing a threshold as the maximum of the reconstruction error on the training normal data is not the right way to identify unseen falls.
According to the G-P method, a cluster of lnC m (r -ln(r -lnrves of normal data is plotted with the incurves of the embeddinormalension m, as shown in Figure 10.
For groups in pairs the non-parametric Mann-Whitney test, or the t test for normal data, were used.
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