Similarity Research Of DNA Sequences With The Cross-fuzzy Entropy

With the successful completion of the human genome project and the advent of the post-gene era,the knowledge system of modern bioinformatics has been continuously expanded,more and more biological information has been mined,and the research work of biological efforts has gradually begun to undergo transformation.From the collection,collation and service of massive biological data,gradually to the comparison,analysis and induction of biological data,so as to better understand the laws of metabolism,development,differentiation and evolution.The study of DNA sequence similarity is an important part of bioinformatics,and it plays a vital role in studying species classification,biological evolutionary relations,structure and function of biological sequences.In this paper,seven kinds of virus DNA sequences are taken as research objects,and a similarity analysis method of DNA sequences based on cross-fuzzy entropy is proposed.The main contents of the paper are as follows:1)The nonlinear characteristics of cross-sample entropy algorithm and cross-fuzzy entropy algorithm are studied.By using the coupled M1 model and the coupled M2 model,the simulation results of the two cross entropy algorithms with different parameters are compared and analyzed.It is found that the stability and anti noise performance of the cross-fuzzy entropy algorithm are better.2)Two DNA representation methods are used to transform seven kinds of virus DNA sequences,and cross-fuzzy entropy algorithm is proposed to analyze the similarity of transformed sequences.The experimental data prove that cross-fuzzy entropy algorithm is feasible to analyze the similarity of DNA sequences.At the same time,compared with cross-sample entropy algorithm,it shows that the robustness of cross-fuzzy entropy algorithm is better than that of cross-sample entropy algorithm.Finally,it is compared with DTW algorithm,which enhances the persuasion of the experiment.3)The dynamic analysis of DNA sequences is carried out by using the sliding window method,and the "most matching" windows of different groups of DNA sequences areobtained by using the cross-fuzzy entropy algorithm.At the same time,the local similarity analysis of homologous DNA sequences is carried out by using the "most matching" window,and the same DNA sequence segments and different base positions are found.Compared with the cross-sample entropy algorithm,the "most matching" window obtained by the cross fuzzy entropy algorithm is better,and the experimental results analyzed are more accurate.