title: Linear regression model of DNA sequences and its application.
abstract: We constructed six new models to analyze the DNA sequences. First, we regarded a DNA primary sequence as a random process in t and gave three ways to define nucleotides' random distribution functions. We extracted some parameters from the linear model and analyzed the changes of the nucleotides' distributions. In order to facilitate the comparison of DNA sequences, we proposed two ways to measure their similarities. Finally, we compared the six models by analyzing the similarities of the DNA primary sequences presented in Table 1 and selected the optimal one.
authors.affiliation: Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, People's Republic of China. daiailiu2004@yahoo.com.cn
authors.forename: Qi
authors.initials: Q
authors.lastname: Dai
authors.forename: Xiao-Qing
authors.initials: XQ
authors.lastname: Liu
authors.forename: Tian-Ming
authors.initials: TM
authors.lastname: Wang
authors.forename: Damir
authors.initials: D
authors.lastname: Vukicevic
mesh_terms: D000818:Animals; D001483:Base Sequence; D004247:DNA / Q000737:chemistry* / Q000145:classification / Q000235:genetics*; D005091:Exons / Q000235:genetics; D005914:Globins / Q000737:chemistry / Q000235:genetics; D006801:Humans; D016014:Linear Models; D008969:Molecular Sequence Data
chemical_list: D005914:Globins; D004247:DNA