Web demos miRNAss


Source code: (1) | R package: (2), user manual (3) | Related publications: [1]

MiRNAss is the first semi-supervised method for microRNA prediction. It is specifically designed to face the problem of scarce pre-miRNAs that can be used as positive examples, in the context of a real prediction task from genome-wide data.

This web-demo allows to test miRNAss method under different conditions and parameters with small datasets. The input parameters are:

  • Features file: comma separate value file with the features extracted from hairpin sequences.
  • P: number of real pre-miRNAs to use as training samples (P>0).
  • N: number of non pre-miRNAs to use as training samples.
  • k: neighbours used to build the nearest neighbour graph.
  • Scaling: apply RELIEF for features scaling.

To test the algorithm with a few number of positive training samples, use the provided sample file (small Human dataset), P in [1…128], N=0, k=10 and no scaling.


Contact: Cristian Yones