## Publications (2)0 Total impact

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**ABSTRACT:**This paper presents a probabilistic approach for DNA sequence analysis. A DNA sequence consists of an arrangement of the four nucleotides A, C, T and G and different representation schemes are presented according to a probability measure associated with them. There are different ways that probability can be associated with the DNA sequence: one way is when the probability of an occurrence of a letter does not depend on the previous one (termed as unsuccessive probability) and in another scheme the probability of occurrence of a letter depends on its previous letter (termed as successive probability). Further, based on these probability measures graphical representations of the schemes are also presented. Using the diagram probability measure one can easily calculate an associated probability measure which can serve as a parameter to check how close is a new sequence to already existing ones.International Journal of Computer Science and Information Security. 01/2010; - [Show abstract] [Hide abstract]

**ABSTRACT:**This paper presents a probabilistic approach for DNA sequence analysis. A DNA sequence consists of an arrangement of the four nucleotides A, C, T and G. There are various representation schemes for a DNA sequence. This paper uses a representation scheme in which the probability of a symbol depends only on the occurrence of the previous symbol. This type of model is defined by two parameters, a set of states Q, which emit symbols and a set of transitions between the states. Each transition has an associated transition probability, aij, which represents the conditional probability of going to state j in the next step, given that the current state is i. Further, the paper combines the different types of classification classes using a Fuzzy composition relation. Finally a log-odd ratio is used for deciding to which class the given sequence belongs to.International Journal of Computer Science and Information Security. 01/2010;