Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

Abstract

Background: MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these ab initio approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.

Results: This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G2DE) based classifier. The G2DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set.

Conclusion: Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G2DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G2DE based predictor.

Figure 1

Parameters of the three generalized Gaussian components generated by G²DE. This figure shows the three generalized Gaussian components of G²DE with the pre-miRNAs in the HU920 dataset and the second feature set. The correlation of interest is indicated with an arrow.

Figure 2

Parameters obtained by basic statistics. These parameters are obtained by calculating the mean, standard deviation and Pearson product-moment correlation coefficients with the pre-miRNAs of the HU920 dataset and the second feature set. The correlation of interest is indicated with an arrow.

Figure 3

Distribution of the HU920 dataset. The x-axis is the first feature of the second feature set, ratio of MFE to the number of stems; the y-axis is the fifth feature of the second feature set, adjusted Shannon entropy. Red ellipses represent the generalized Gaussian components shown in Figure 1; the black ellipse represents the Gaussian component shown in Figure 2. The red squares and green circles represent the pre-miRNAs and the pseudo hairpins, respectively. Values within the parentheses indicate the correlations between these two features in the corresponding Gaussian components.