Plant MicroRNA-mRNA Target Prediction Using Support Vector Machine

• Main Authors: Shu-Yu Kang, 康書語
• Other Authors: Chien-Kang Huang
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/80065420999427175285

碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 101 === Plant microRNAs (miRNAs) are small non-coding RNAs consisting of 19-22 nucleotides. MiRNAs play an important role in gene regulation and affect many follow-up biological interactions either by suppressing the translation of target genes to proteins or by the cleavage of the target genes. Due to the costly and time-consuming biochemical experiment process to verify a target gene, computational methods are developed to screen out candidates that are not likely to be the targets. Most current prediction tools develop their algorithm based on six categories of features that are commonly recognized and reported to be important in miRNA-mRNA interactions. These six categories are complementarity, thermodynamic stability for duplex, site accessibility, evolutionary conservation, site location and multiplicity of binding sites. In this research, all the six categories of features along with proposed features are considered. This research uses machine learning based algorithms “Support Vector Machine (SVM)” as classifier to predict plant miRNA binding targets, followed by a feature selection phase using RELIEF-F method. In an independent test on Arabidopsis thaliana, the proposed tool can achieve the prediction result with the precision of 100%, accuracy of 97.8%, sensitivity of 97.1%, and specificity of 100%. Moreover, according to the result of RELIEF-F scores in feature selection, minimum free energy (MFE) of miRNA-mRNA duplex appears to be the most important feature, followed by the bigram and trigram features.