Abstract
The application of one-class machine learning is gaining attention in the computational biology community. Different studies have described the use of two-class machine learning to predict microRNAs (miRNAs) gene target. Most of these methods require the generation of an artificial negative class. However, designation of the negative class can be problematic and if it is not properly done can affect the performance of the classifier dramatically and/or yield a biased estimate of performance. We present a study using one-class machine learning for miRNA target discovery and compare one-class to two-class approaches. Of all the one-class methods tested, we found that most of them gave similar accuracy that range from 0.81 to 0.89 while the two-class naive Bayes gave 0.99 accuracy. One and two class methods can both give useful classification accuracies. The advantage of one class methods is that they don’t require any additional effort for choosing the best way of generating the negative class. In these cases one- class methods can be superior to two-class methods when the features which are chosen as representative of that positive class are well defined.
Highlights
MicroRNAs are single-stranded, non-coding RNAs averaging 21 nucleotides in length
We present a study using one-class machine learning for miRNA target discovery and compare one-class to two-class approaches
Several computational approaches have been applied to miRNA gene prediction using methods based on sequence conservation and/or structural similarity [3,4,5, 6,7]. Those methods that used machine learning were based on the two-class approaches, while our new reported results are based on the one-class approaches
Summary
MicroRNAs (miRNAs) are single-stranded, non-coding RNAs averaging 21 nucleotides in length. Recent findings [2] suggest microRNAs may affect gene expression by binding to either 5’ or 3’ untranslated regions (UTRs) of mRNA, most studies have found that miRNA mark their target mRNAs for degradation or suppress their translation by binding to the. Several computational approaches have been applied to miRNA gene prediction using methods based on sequence conservation and/or structural similarity [3,4,5, 6,7]. Those methods that used machine learning were based on the two-class approaches, while our new reported results are based on the one-class approaches
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