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‡Best method for the dataset.
From the DREAM4 challenge, we present the results of our method for the dataset Insilico_Size_100 subchallenge, which can be downloaded from the DREAM website (http://wiki.c2b2.columbia.edu/dream/index.php/The_DREAM_Project).edu/dream/index.php/The_DREAM_Project
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Factor analysis with varimax rotation was conducted as the first method for the datasets.
The estimated predictive accuracy for each method for the datasets with outliers was compared with the estimates for the corresponding datasets without outliers.
Regarding the accuracy, Tables 1 and 2 list the segmentation sensitivity of the different methods for the dataset used in the experiment.
The number in bold indicates the highest R2 among three methods for the dataset.
For each row, the number in bold indicates the highest R2 among different methods for the dataset Illumina sequencing of Human liver transcripts.
The gene expression data were normalized using the quantile method (for the BeadArray dataset) or the RMA method (for the Affymetrix datasets).
The baseline method was slightly worse than the proposed method for the KTH dataset.
Figure 4 Objective perceptual quality measures obtained with our proposed method for the entire dataset.
The accuracy of our method for the training dataset was 100.0%, and for the 10-fold cross-validation was 93.1%.
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