Structured Sparse Regularization-Based Deep Fuzzy Networks for RNA N6-Methyladenosine Sites Prediction

In many biological processes, N6-methyladenosine (m6A) plays a critical role. Experimental methods for identifying m6A sites have proven to be costly, and existing computational methods still require improvement. To address these challenges, we develop a novel computational method called structured sparse regularization-based fuzzy hierarchical echo state network to identify m6A sites in mammals. We apply fuzzy systems to deep learning. Compared with traditional fuzzy inference systems, this deep fuzzy network has the ability to generate feature representations. Echo state network (ESN) is a special type of recurrent neural network, which consists of an input layer, a randomly generated large fixed hidden layer (called a reservoir), and an adaptive output layer. The advantages of our method over ESNs are that it is capable of mining and capturing hidden features layer-by-layer within reservoirs and has better approximation performance. In order to remove redundancy, the output layer weights are trained by structured sparse learning, which enhances the generalizability and robustness of the method. Evaluation of our method by testing it on tissue-specific datasets shows that it outperforms existing tools.