Application of General Information Model to DNA Identification
Journal Title: Biomedical Journal of Scientific & Technical Research (BJSTR) - Year 2019, Vol 18, Issue 3
Abstract
We deduce the Fisher information equation in terms of DNA research from its formal definition. By finding the similar rules corresponding to different methods to measure information, we apply the general information model to four kinds of information quantities and attain respective equations in DNA research domain. Then we find that the essence of information quantities is the measurement of similar rules of systems, e.g., we discover that Shannon’s entropy is a kind of description on the combination of some different similar rules with respective weights. Because of the difference of measurement, the similar rules of Shannon’s entropy differ from those of Fisher information. We discuss the relations between each of the four information quantities and the k-tuple word length k of DNA sequences of 16 typical genomes, respectively. Then we find that the difference between Shannon’s entropy versus k and Fisher information versus k may be in accordance with the discrepancy between their similar rules. Since DNA sequences are generally close to random sequences, the similar rules of Shannon’s entropy of different living organisms in word domain are similar and such similarity provides a convincing explanation to the universal linear relation existing among the studied species. Research on DNA sequences is the focus of modern life science. Analysis of statistical attributes of DNA sequences is significant for evolutional biology and for technologies to identify living organisms. Several attempts have been made to identify relatively small size (microbial) genomes by using the distribution of the appearance of short consecutive nucleotide strings of length k called k-tuple [1-7]. To describe the distribution, many scholars used information quantities including Shannon entropy and Fisher information [8-16]. Some scholars also utilized some nonlinear methods or models to analyze the DNA sequences [17,18]. In this paper, we generalize the geographical remote sensing information model [19] to a general information model which is image joining equation calculated. Such model is a grey non-linear equation that is from formal logic inferring to dialectical logic calculation and from abstract thinking to both of abstract and visual thinking [19]. We apply the general information model to deduce four kinds of information quantities used in DNA sequence analysis and then analyze the statistical results cited from Ref. [15] on the scope of similar rules.The remote sensing information model is established according to geographical regularity and complexity. This model, a grey nonlinear equation, combines both certainty and uncertainty in an equation from the point of view of dialectical logic. In geographic research, scholars usually transform non-remote sensing data into images and then generalize the remote sensing information model to the geographical image information model. Here, we take any data that can be portrayed as images into account and apply the model further to any field that is characteristic of complexity and non-linearity as geography. Then we obtain a general information model. Based on the analysis in terms of the formal logic principle, (Table 1) gives the analysis of the remote sensing information model and the general information model on intension and extension of certainty and uncertainty [19]. In logic, intension is the set of attributes constituting the meaning of a term [20].
Authors and Affiliations
Li Ying Wang, Meng Suo, Yong Chang Huang
Keywords
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- EP ID EP620676
- DOI 10.26717/BJSTR.2019.18.003164
- Views 192
- Downloads 0
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