Plant miRNA-lncRNA Interaction Prediction with the Ensemble of CNN and IndRNN

Non-coding RNA (ncRNA) plays an important role in regulating biological activities of animals and plants, and the representative ones are microRNA (miRNA) and long non-coding RNA (lncRNA). Recent research has found that predicting the interaction between miRNA and lncRNA is the primary task for elucidating their functional mechanisms. Due to the small scale of data, a large amount of noise, and the limitations of human factors, the prediction accuracy and reliability of traditional feature-based classification methods are often affected. Besides, the structure of plant ncRNA is complex. This paper proposes an ensemble deep-learning model based on convolutional neural network (CNN) and independently recurrent neural network (IndRNN) for predicting the interaction between miRNA and lncRNA of plants, namely, CIRNN. The model uses CNN to explore the functional features of gene sequences automatically, leverages IndRNN to obtain the representation of sequence features, and learns the dependencies among sequences; thus, it overcomes the inaccuracy caused by human factors in traditional feature engineering. The experiment results show that the proposed model is superior to shallow machine-learning and existing deep-learning models when dealing with large-scale data, especially for the long sequence.