基于BP神经网络的DNA解链温度预测模型

Predicting Melting Temperature of DNA Duplex by BP Neural Network

在DNA计算中,为了确保计算过程的可靠性,要求编码信息的DNA序列必须具有相似的热力学稳定性。解链温度是目前评价DNA序列热力学稳定性的一个主要的参数,目前,生物工程中常用的各种预测方法都存在某些序列的误差偏大的缺点,因此难以满足像DNA计算这种大量DNA序列进行各种生化反应的计算过程的要求。论文以DNA序列的邻近法参数为基础,建立了一个基于BP神经网络的解链温度的预测模型。计算结果表明,DNA序列的解链温度的误差可以达到±5.5℃的范围。

In DNA computing,one requirement of the encoding DNA sequences is that they should keep similar thermodynamic stability in order to maintain the reliability of the computing process,Melting temperature is a suitable parameter used to evaluate the thermodynamic stability of DNA sequences.As traditional predicting methods used in biological engineering may exist lager error for a few sequences,thus it can＇t meet situation involved large amount of DNA sequences as DNA computing.In this paper,we introduce a BP Neural Network to predict the melting temperature based on the Nearest-Neighbor parameters.Our result shows that the predicting error can be limited within ±5.5℃.