miRNAss: Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples
Machine learning method specifically designed for
pre-miRNA prediction. It takes advantage of unlabeled sequences to improve
the prediction rates even when there are just a few positive examples, when
the negative examples are unreliable or are not good representatives of
its class. Furthermore, the method can automatically search for negative
examples if the user is unable to provide them. MiRNAss can find a good
boundary to divide the pre-miRNAs from other groups of sequences; it
automatically optimizes the threshold that defines the classes boundaries,
and thus, it is robust to high class imbalance. Each step of the method is
scalable and can handle large volumes of data.
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