Novel ncRNA gene prediction method based on fuzzy neural network

An effective computational approach for novel non-coding RNA (ncRNA) gene prediction based on fuzzy neural networks is presented. It consists of three modules, including a genome sequence preprocessor, a fuzzy neural network with structure learning predictor (FNNSL), and a postprocessor. The preprocessor converts the input sequence alignment to a sequence of sliding windows, and extracts effective features for each window. The five-layer structure of Takagi-Sugeno type fuzzy neural network is adopted for the predictor. Based on the fuzzy partitioning of the features and the definition of membership functions of fuzzy subsets, the prediction results are obtained by the computation of the input layer, the fuzzifying layer, the firing strength layer, the normalized firing strength layer, and the output layer. Furthermore, a structure learning algorithm is introduced to decrease parameter dimensions, enhance the computational efficiency, and avoid the over-learning. Finally, the postprocessor stitches overlapping predictions together. The experimental results show that the prediction accuracy of this method is higher than other ncRNA gene prediction tools.