A Study of RNA Features for MicroRNA Target Prediction
Date Issued
2011
Date
2011
Author(s)
Kung, De-Mao
Abstract
MicroRNAs (miRNAs), which are belonged to small non-coding RNA molecules, play an important role in post transcriptional gene regulation. MiRNAs suppress the translation of target genes to proteins, leading to affect many follow-up biological interactions. Computational methods of miRNA target prediction have been developed to reduce costly and time-consuming biochemical experiments. According to currently known knowledge of biology, six primary attributes of miRNA-mRNA interaction are employed in the approaches of miRNA target prediction: seed complementarity, thermodynamic stability for duplex, site accessibility, evolutionary conservation, site location and multiplicity of binding sites.
In our study, we propose a comprehensive method depending on eight feature categories including the six feature categories and two proposed categories, non Watson-Crick pairing and compactness. We extract these features and utilize two machine learning based algorithms, Support Vector Machine (SVM) and Random Forest, as the classifiers to predict human miRNA targets. Incorporated the training and independent testing datasets, we evaluate our performance compared with other current miRNA target prediction methods and demonstrate the importance of RNA features for miRNA target prediction by RELIEF-F method in feature selection. The results of our method outperform other predictors in the comparisons of performance, with the evaluation indexes: precision of 91.4%, accuracy of 78.5%, sensitivity of 79.1%, and specificity of 76.3%. Moreover, as the result of RELIEF-F scores in feature selection, minimum free energy (MFE) of miRNA-mRNA duplex is the most significant RNA feature in miRNA target prediction, and the importance of composition and site accessibility are shown as well.
Subjects
microRNA
target gene prediction method
feature extraction
machine learning
feature selection
Type
thesis
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