Prediction accuracies of five individual predictors in the D163 and D1679 datasets.

<p>Prediction accuracies of five individual predictors in the D163 and D1679 datasets.</p

Comparison between mirMeta and HetroMirPred.

<p>Comparison between mirMeta and HetroMirPred.</p

Comparison of true predictions between every two individual predictors for all the 1679 positive samples in the D1679 dataset.

<p>Comparison of true predictions between every two individual predictors for all the 1679 positive samples in the D1679 dataset.</p

Comparison of true predictions between every two individual predictors for all the 163 positive samples in the D163 dataset.

<p>Comparison of true predictions between every two individual predictors for all the 163 positive samples in the D163 dataset.</p

Performance of meta-predictors under multi-fold cross validation and in independent dataset under preprocess-II transformation strategy.

<p>Performance of meta-predictors under multi-fold cross validation and in independent dataset under preprocess-II transformation strategy.</p

Infrastructure of the meta-predictor.

<p>Query sequence is input into each individual predictor. The outputs of individual predictors are preprocessed and then fed into an ANN to make a new prediction, which is the output of meta-predictor. Therefore, the meta-predictor is composed of individual predictors, preprocessing modules, and ANN. The parameters of ANN will be trained using datasets containing both positive and negative samples of miRNAs. Although five individual predictors were shown in the figure, the meta-predictor could be made from any number of individual predictors out of five. The total number of possible meta-predictors is 26. The final meta-predictor mirMeta contains all five individual predictors.</p

Performance of meta-predictors using preprocess-I transformation under multi-fold cross validation and in independent dataset.

<p>Performance of meta-predictors using preprocess-I transformation under multi-fold cross validation and in independent dataset.</p

Comparison of true predictions between every two individual predictors for all the 168 negative samples in the D163 dataset.

<p>Comparison of true predictions between every two individual predictors for all the 168 negative samples in the D163 dataset.</p

Comparison of true predictions between every two individual predictors for all the 674 negative samples in the D1679 dataset.

<p>Comparison of true predictions between every two individual predictors for all the 674 negative samples in the D1679 dataset.</p

Non-linear transformations change the distribution of ProMiR prediction scores of all the samples in the D1679 dataset.

<p>The upper panels show the distribution of raw prediction scores of ProMiR for positive samples (a) and negative samples (b). The inset in (b) is the distribution of scores for negative samples when x-axis is scaled using logarithm. The intermediate panels present the distribution of prediction scores after preprocess-I transformation for positive samples (I-a) and negative samples (I-b). The lower panels are scores after preprocess-II transformation for positive samples (II-a) and negative samples (II-b).</p