Increasing evidences have shown that microRNAs (miRNAs) play important roles in many diseases. The environmental factors (EFs) can regulate the expression level of miRNAs in human tissues. Therefore, identifying potential miRNA-environmental factor interactions is helpful not only for understanding the pathogenesis of diseases, but also for disease diagnosis, prognosis and treatment. In this paper, we propose a computational framework, MEI-BRWMLL (MiRNA-EF Interaction prediction based on Bi-Random walk and Multi-Label Learning), to identify interactions between miRNAs and environmental factors. The sequence and topology information of miRNA and structure, anatomical therapeutic chemical and topology information of environmental factor are employed to measure similarity of miRNAs and environmental factors, respectively. In addition, we use similarity network fusion method to integrate biological information of miRNAs and environmental factors, respectively. In the last, the bi-random walk and multi-label learning method are utilized to identify potential miRNA-environmental factor interactions. In order to evaluate the performance of MEI-BRWMLL, we implement the ten-fold cross validation in the experiment. The MEI-BRWMLL achieves an AUC of 0.8208. It has been shown that MEI-BRWMLL is able to identify known miRNA-environmental factor interactions.
['Wei Lan', 'Jianxin Wang', 'Min Li', 'Chengqian Lu', 'Fang-Xiang Wu', 'Yi Pan']
Predicting microRNA-environmental factor interactions based on bi-random walk and multi-label learning