  Computational discovery of microRNAs (miRNA) is based on pre-determined sets
of features from miRNA precursors (pre-miRNA). These feature sets used by
current tools for pre-miRNA recognition differ in construction and dimension.
Some feature sets are composed of sequence-structure patterns commonly found in
pre-miRNAs, while others are a combination of more sophisticated RNA features.
Current tools achieve similar predictive performance even though the feature
sets used - and their computational cost - differ widely. In this work, we
analyze the discriminant power of seven feature sets, which are used in six
pre-miRNA prediction tools. The analysis is based on the classification
performance achieved with these feature sets for the training algorithms used
in these tools. We also evaluate feature discrimination through the F-score and
feature importance in the induction of random forests. More diverse feature
sets produce classifiers with significantly higher classification performance
compared to feature sets composed only of sequence-structure patterns. However,
small or non-significant differences were found among the estimated
classification performances of classifiers induced using sets with
diversification of features, despite the wide differences in their dimension.
Based on these results, we applied a feature selection method to reduce the
computational cost of computing the feature set, while maintaining discriminant
power. We obtained a lower-dimensional feature set, which achieved a
sensitivity of 90% and a specificity of 95%. Our feature set achieves a
sensitivity and specificity within 0.1% of the maximal values obtained with any
feature set while it is 34x faster to compute. Even compared to another feature
set, which is the computationally least expensive feature set of those from the
literature which perform within 0.1% of the maximal values, it is 34x faster to
compute.
