A Novel ncRNA Gene Prediction Approach Based on Fuzzy Neural Networks with Structure Learning

Discovering ncRNA genes is a challenging problem, which has attracted much attention recently. The accuracy of computational ncRNA prediction methods still needs to be improved, however, due to the diversity and the lack of consensus patterns of ncRNA genes. In this paper, we propose an effective computational approach based on fuzzy neural networks with structure learning (FNNSL) for novel ncRNA gene prediction. It has advantages such as explicit physical meanings of nodes and parameters in the network, and effective incorporation of prior knowledge by the fuzzy sets theory. Specifically, a structure learning algorithm is presented to decrease parameter dimensions, enhance the computational efficiency, and avoid the over-learning. In addition, a fuzzy c-means clustering method is adopted for fuzzy partitioning of input feature variables, and the corresponding implementations are compared to the other ncRNA gene prediction tools. The improved prediction accuracy demonstrates the effectiveness of the proposed approach.