RNA二级结构预测的神经网络方法

针对利用经典的随机上下文无关文法(SCFG)等模型对RNA(R ibonucle ic ac id)二级结构进行预测时,存在计算复杂性问题,该文给出了RNA二级结构的“新二级结构单元标签”(N SSEL)表示,相应提出了一种新的RNA二级结构预测的神经网络方法。这种二级结构的N SSEL表示格式很容易转换成常用的CT格式。基于tRNA数据集的实验表明,在完全相同的训练与测试数据集下,该方法,较之性能最好的B JK与BK 2等SCFG模型,其预测精度与相关系数都有所提高,证明了所提方法的可行性与有效性。由于神经网络启发式方法不存在计算时间复杂性问题,因此可望将此法用于预测SCFG等算法难以处理的大于1 000个碱基的长RNA序列的折叠问题。

Neural network approach to predict RNA secondary structures

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(1 000) nt,which are difficult with traditional folding algorithms.