Plant microRNA-target interaction identification model based on the integration of prediction tools and support vector machine.

ABSTRACT:

Background

Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA-target interactions.

Results

Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previous

SUBMITTER: Meng J 

PROVIDER: S-EPMC4106887 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature