Grouping\Clustering miRNAs in non-overlapped groups

Question

[Na Sed]

I am trying to analyze the similarity between miRNAs and putting them in some distinct groups, non-overlapped groups, based on their similarities.

I need some suggestions about the measure of similarity, for example one of them can be similarity in their sequences.

Of course, I have expression profile of them, so another way to categorize them is clustering based on expression analysis.

Now, I want to know is there any other idea for considering as similarity measure?

Answer 1

[Ido Tamir]

http://nar.oxfordjournals.org/content/37/suppl_1/D111.full

This was for miRNA prediction from sequence hairpins, but maybe some of the features for the svm can be used by you:

We investigated a number of the most discriminating features, such as the minimum free-energy index (34) or the mean base pair distance in the ensemble of structures, and trained an SVM (support vector machine) classifier using LIBSVM (http://www.csie.ntu.edu.tw/%7Ecjlin/libsvm). The total number of features used for this first SVM was 253.

Minimum free energy, conserved stem etc..