加窗窄通带滤波器蛋白质编码区预测算法

摘要

改进Gabor小波变换(Modified Gabor wavelet transform,MGWT)蛋白质编码区预测算法给出的预测结果在当前所有的独立预测算法中准确率最高.本文提出了有限脉冲响应(Finite impulse response,FIR)加窗窄通带滤波器蛋白质编码区预测(Windowed narrow pass-band filter,WNPBF)算法.算法的主要部分包括:以F56F11.4序列为例给出了WNPBF阶数选择的依据,并据此设计WNPBF;为消除滤波器群延迟对预测结果产生的不良影响,对信号用边界对称延拓法进行预处理并对窄带滤波器滤波输出信号进行截取;为改善预测结果,设计滑动平均滤波器平滑功率谱密度曲线.在ALLSEQ和HMR195两个DNA序列集上获得预测准确率分别接近或达到独立预测算法的最高水平.通过比较后发现,所提出的WNPBF算法较MGWT算法效率更高,使用该算法可以直观和客观地比较不同滤波器的预测结果.%The modified Gabor wavelet transform (MGWT) algorithm provides the best prediction results among the independent protein coding regions prediction algorithms. To be compared, a finite impulse response (FIR) windowed narrow pass-band filter (WNPBF)-based protein coding regions prediction algorithm is proposed. The algorithm is mainly consisted of the following parts:designing a WNPBF with appropriate length, which taking the DNA sequence F56F11. 4 as an example; extending the DNA sequences using the boundary symmetric padding method and cutting off the beginning part of WNPBF outputs to eliminate the side effects of the group delay of the filter on prediction results; designing a moving average filter to smooth the power spectral density curve to get better prediction results. Experiments performed on DNA data sets ALLSEQ and HMR195 respectively give the prediction results which are close or equal to the best ones. The proposed algorithm is much more efficient than MGWT algorithm and can be used to compare the prediction results of different filters directly and objectively.