جستجوی مقالات مرتبط با کلیدواژه "بیوانفورماتیک" در نشریات گروه "ریاضی"

Phylogenetics is a field in bioinformatics that categorizes and structures the evolutionary hisotiry of currently living species. Phylogenetics uses network tools to represent evolutionary histories in a comprehensive model. The simpleset possible network model is a tree model that is called a phylogenetic tree. Phylogenetic trees are divided into two categories, which are rooted and un-rooted. In this research, rooted phylogenetic trees are of interest. There are different types of input for constructing rooted phylogenetic trees. Rooted triplets are one of the important inputs for constructing rooted phylogenetic trees, which are used in this study. A rooted triplet is a rooted binary tree with three leaves. The rooted triplet is the most simplest model, which contains valuable information. The problem of building a rooted phylogenetic tree that includes the maximum number of a set of rooted triplets is an optimizataion problem, known as MRTC problem. The important challenge in phylogenetics is that MRTC is NP-hard. Therefore, the focus of this research is to introduce a novel algorithm for MRTC problem. The aim of the new algorithm is to improve the consistency of input rooted triplets with the final rooted phylogenetic tree. In order to indicate the performance of the new algorithm, it is compared with on of the best methods on biological data which is called TRH. The Experimental results on triplets that are obtained from biological data show that our new algorithm outperforms TRH base on time complexity and consistency.

In this paper, we consider a three parametric regularly varying generalized Hypergeometric distribution which have been generated by Birth-Death process for describing phenomena in bioinformatics (Danielian and Astola, 2006). Under satisfying some conditions, we obtain the system of likelihood equations which its solution coincides with the maximum likelihood estimators. The given maximum likelihood estimators are the same as some moment estimators.Moreover, an approximate computation of the maximum likelihood estimations for the unknown parameters is given. Using MCMC, simulation studies are proposed. Finally, in order to present applications, some real data sets in bioinformatics are fitted with the model. Based on some important criterions, this model is compared with four other discrete distributions in bioinformatics. We see that the generalized Hypergeometric distribution provides a better fit than four other discrete distributions.