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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 previously existing methods. The results were validated by using degradome sequencing supported  Arabidopsis thaliana  miRNA-target interactions. The proposed model constructed on  Arabidopsis thaliana  was run over  Oryza sativa  and  Vitis vinifera  to demonstrate that our model is effective for other plant species.    Conclusions  The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided.

Plant microRNA-Target Interaction Identification Model Based on the Integration of Prediction Tools and Support Vector Machine