Classification of RNA Secondary Structure Using a Novel Feature-Extraction and Neural Network

摘要

RNA has recently become the center of much attention because of its catalytic properties, leading to an increased interest in obtaining structural information. This suggests that development of computational tools based on RNA secondary structure is essential for discovery of new non-coding RNAs and classification of their functional roles. In this paper, first we introduce a new method for featureextraction from a RNA secondary structure sequence; next we use MLP neural networks for classification of six families from Rfam data base. Achieved experiment results show that our represented method against previous works on classifying of RNA secondary structure has been improved and the structural complexity desirably has been decreased.