Inferring pseudogene–MiRNA associations based on an ensemble learning framework with similarity kernel fusion

Accumulating evidence shows that pseudogenes can function as microRNAs (miRNAs) sponges and regulate gene expression. Mining potential interactions between pseudogenes and miRNAs will facilitate the clinical diagnosis and treatment of complex diseases. However, identifying their interactions through biological experiments is time-consuming and labor intensive. In this study, an ensemble learning framework with similarity kernel fusion is proposed to predict pseudogene–miRNA associations, named ELPMA. First, four pseudogene similarity profiles and five miRNA similarity profiles are measured based on the biological and topology properties. Subsequently, similarity kernel fusion method is used to integrate the similarity profiles. Then, the feature representation for pseudogenes and miRNAs is obtained by combining the pseudogene–pseudogene similarities, miRNA–miRNA similarities. Lastly, individual learners are performed on each training subset, and the soft voting is used to yield final decision based on the prediction results of individual learners. The k-fold cross validation is implemented to evaluate the prediction performance of ELPMA method. Besides, case studies are conducted on three investigated pseudogenes to validate the predict performance of ELPMA method for predicting pseudogene–miRNA interactions. Therefore, all experiment results show that ELPMA model is a feasible and effective tool to predict interactions between pseudogenes and miRNAs.