<p>(A)ROC curve displaying the performance of different ML-based miRNA predictors in the ten-fold cross-validation experiment.(B) Performance of ML-based miRNA predictors obtained with different numbers of features.(C) Distribution of the number of base pairs in the positive and negative sample sets.(D) Distribution of the average number of base pairs in a 4-nt sliding window in the positive and negative sample sets.(E) Frequency of bulges 1nt upstream of the miRNA end in the positive and negative sample sets.</p
<p>The experimentally confirmed data and perturbation data are used for the validation in the MCC da...
<p>Receiver operator characteristic curve analysis of individual miRNAs (A: miR423-5p; B: miR30d; C:...
<p>Measured by the area under the ROC curve (AUC), classification performance is shown for models co...
<p>The true positive rate (TPR) is plotted as a function of false positive rate (FPR) for the 7 miRN...
<p>A. Effect of increasing number of ss-motifs on miRNA prediction accurracy. Note: The X-axis is di...
<p>Number of significant models, scaled mean squared prediction error from cross-validation (cv-SMSE...
<p>T1 and T2 data for the re-analysis of the MiRFinder study.</p> <p><strong>testdata.tar.bz2</stron...
MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus...
<p>Shown is the mean performance for each machine learning method (see <a href="http://www.plosone.o...
<p>(A) The distribution and comparison of functional similarity scores of intrafamily, interfamily, ...
<p>AUC values of miRNA predictors constructed with different ML algorithms using different RPNSs.</p
a<p>5′-strand indicates that the mature sequence locates on the 5′ arm of the stem-loop structure of...
<p>AUC, area under the curve; S.E., standard error; 95% C.I., 95% confidence interval.</p
<p>Evaluation of the performance of the 24-miRNA classifier (left) and a classifier including the 24...
Gene regulation modulates RNA expression via transcription factors. Posttranscriptional gene regulat...
<p>The experimentally confirmed data and perturbation data are used for the validation in the MCC da...
<p>Receiver operator characteristic curve analysis of individual miRNAs (A: miR423-5p; B: miR30d; C:...
<p>Measured by the area under the ROC curve (AUC), classification performance is shown for models co...
<p>The true positive rate (TPR) is plotted as a function of false positive rate (FPR) for the 7 miRN...
<p>A. Effect of increasing number of ss-motifs on miRNA prediction accurracy. Note: The X-axis is di...
<p>Number of significant models, scaled mean squared prediction error from cross-validation (cv-SMSE...
<p>T1 and T2 data for the re-analysis of the MiRFinder study.</p> <p><strong>testdata.tar.bz2</stron...
MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus...
<p>Shown is the mean performance for each machine learning method (see <a href="http://www.plosone.o...
<p>(A) The distribution and comparison of functional similarity scores of intrafamily, interfamily, ...
<p>AUC values of miRNA predictors constructed with different ML algorithms using different RPNSs.</p
a<p>5′-strand indicates that the mature sequence locates on the 5′ arm of the stem-loop structure of...
<p>AUC, area under the curve; S.E., standard error; 95% C.I., 95% confidence interval.</p
<p>Evaluation of the performance of the 24-miRNA classifier (left) and a classifier including the 24...
Gene regulation modulates RNA expression via transcription factors. Posttranscriptional gene regulat...
<p>The experimentally confirmed data and perturbation data are used for the validation in the MCC da...
<p>Receiver operator characteristic curve analysis of individual miRNAs (A: miR423-5p; B: miR30d; C:...
<p>Measured by the area under the ROC curve (AUC), classification performance is shown for models co...