QQ客服 官方微博 反馈留言

	Neural network approach to predict RNA secondary structures
	Zhang Xiuwei ; Deng Zhidong ; Song Dandan
	2010-05-06 ; 2010-05-06
关键词	Practical Theoretical or Mathematical/ backpropagation biology computing computational complexity context-free grammars macromolecules neural nets organic compounds/ RNA secondary structures ribonucleic acid stochastic context-free grammar BP neural network computational complexity/ A8715B Biomolecular structure, configuration, conformation, and active sites C7330 Biology and medical computing C4210L Formal languages and computational linguistics C5290 Neural computing techniques C4240C Computational complexity
中文摘要	Ribonucleic acid (RNA) secondary structure predictions based on stochastic context-free grammar (SCFG) models are very complex. This paper presents a BP neural network approach for predicting RNA secondary structures based on a new representation of the RNA structure information. The new format for the secondary structure prediction results can be easily converted to the commonly used CT format. Test results obtained with tRNA training and testing datasets show that the approach has higher prediction accuracy and greater correlation coefficients than the two best-performance SCFG models. Since computational complexity for heuristic neural network approaches are relatively simple, the method can be used to solve secondary structure prediction problems of long RNA sequences with lengths greater than 1000 nt, which are difficult with traditional folding algorithms.
语种	中文 ; 中文
出版者	Tsinghua Univ. Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/9814]
专题	清华大学
推荐引用方式 GB/T 7714	Zhang Xiuwei,Deng Zhidong,Song Dandan. Neural network approach to predict RNA secondary structures[J],2010, 2010.
APA	Zhang Xiuwei,Deng Zhidong,&Song Dandan.(2010).Neural network approach to predict RNA secondary structures..
MLA	Zhang Xiuwei,et al."Neural network approach to predict RNA secondary structures".(2010).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。