Classification of RNA Secondary Structure Using a Novel Feature-Extraction and Neural Network
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.
feature extraction RNA secondary structure RNA classification neural networks
Amir Hosein Kashefi Mostafa Noruzi Nashalji Vahideh Keikha
Department of Computer Science, University of Tehran, Iran Tehran, Iran Research ID: C-7636-2009, Islamic Azad University, South Tehran Branch, Young Researchers Club, Tehr Department of Computer Science, University of Sistan and Baluchestan, Zahedan, Iran, Zahedan, Iran
国际会议
三亚
英文
379-382
2011-03-25（万方平台首次上网日期，不代表论文的发表时间）