【摘要】 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。更多还原

【Abstract】 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℃.更多还原