Analysis of Machine Learning Based Methods for Identifying MicroRNA Precursors

MicroRNAs are a type of non-coding RNA that were discovered less than a decade ago but are now known to be incredibly important in regulating gene expression despite their small size. However, due to their small size, and several other limiting factors, experimental procedures have had limited success in discovering new microRNAs. Computational methods are therefore vital to discovering novel microRNAs. Many different approaches have been used to scan genomic sequences for novel microRNAs with varying degrees of success. This work provides an overview of these computational methods, focusing particularly on those methods based on machine learning techniques. The results of experiments performed on several of the machine learning based microRNA detectors are provided along with an analysis of their performance

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test

In silico prediction of non-coding RNAs using supervised learning and feature ranking methods

This thesis presents a novel method, RNAMultifold, for development of a non-coding RNA (ncRNA) classification model based on features derived from folding the consensus sequence of multiple sequence alignments using different folding programs: RNAalifold, CentroidFold, and RSpredict. The method ranks these folding features according to a Class Separation Measure (CSM) that quantifies the ability of the features to differentiate between samples from positive and negative test sets. The set of top-ranked features is then used to construct classification models: Naive Bayes, Fisher Linear Discriminant, and Support Vector Machine (SVM). These models are compared to the performance of the same models with a baseline feature set and with an existing classification tool, RNAz. The Support Vector Machine classification model with a radial basis function kernel, using the top 11 ranked features, is shown to be more sensitive than other models, including another ncRNA prediction program, RNAz, across all specificity values for the RNA families under study. In addition, the target feature set outperforms the baseline feature set of z score and structure conservation index across all classification methods, with the exception of Fisher Linear Discriminant. The RNAMultifold method is then used to search the genome of a Trypanosome species (Trypanosoma brucei) for novel ncRNAs. The results of this search are compared with known ncRNAs and with results from RNAz