<p>A total of 48 features belonging to 3 categories are extracted to classify high quality miRNA target interactions from false positive ones. All features mentioned are widely accepted to predict miRNA-target interactions and discriminate creditable targets from false positive ones.</p
Motivation: MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potent...
MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus...
<p>(A)ROC curve displaying the performance of different ML-based miRNA predictors in the ten-fold cr...
<p>Our approach is mainly divided into two steps: Online prediction and local classification. PsRNAT...
Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction...
Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction...
BACKGROUND: Machine learning based miRNA-target prediction algorithms often fail to obtain a balance...
<p>SVM classification with 10-fold cross validation to classify samples into normal, primary or meta...
a<p>Total number of miRNA.</p>b<p>miRNA target interactions gained by predictors.</p>c<p>miRNA targe...
<p>The experimentally confirmed data and perturbation data are used for the validation in the MCC da...
<p>Average SVM classification accuracy of kernel and non-kernel based methods varying the number of ...
<p>Measured by the area under the ROC curve (AUC), classification performance is shown for models co...
Confident identification of microRNA-target interactions is significant for studying the function of...
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally vi...
MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression and biological processes ...
Motivation: MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potent...
MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus...
<p>(A)ROC curve displaying the performance of different ML-based miRNA predictors in the ten-fold cr...
<p>Our approach is mainly divided into two steps: Online prediction and local classification. PsRNAT...
Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction...
Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction...
BACKGROUND: Machine learning based miRNA-target prediction algorithms often fail to obtain a balance...
<p>SVM classification with 10-fold cross validation to classify samples into normal, primary or meta...
a<p>Total number of miRNA.</p>b<p>miRNA target interactions gained by predictors.</p>c<p>miRNA targe...
<p>The experimentally confirmed data and perturbation data are used for the validation in the MCC da...
<p>Average SVM classification accuracy of kernel and non-kernel based methods varying the number of ...
<p>Measured by the area under the ROC curve (AUC), classification performance is shown for models co...
Confident identification of microRNA-target interactions is significant for studying the function of...
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally vi...
MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression and biological processes ...
Motivation: MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potent...
MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus...
<p>(A)ROC curve displaying the performance of different ML-based miRNA predictors in the ten-fold cr...
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