Sharif Digital Repository

Disease association study is of great importance among various fields of study in bioinformatics. Computational methods happen to be advantageous specifically when experimental approaches fail to obtain accurate results. Haplotypes are believed to be the most responsible biological data for genetic diseases. In this paper, the problem of reconstructing haplotypes from error-containing SNP fragments is discussed For this purpose, two new methods have been proposed by a combination of k-means clustering and particle swarm optimization algorithm. The methods and their implementation results on real biological and simulation datasets are represented which shows that they outperform the methods... 

Reconstructing haplotype in MEC (Minimum Error Correction) model is an important clustering problem which focuses on inferring two haplotypes from SNP fragments (Single Nucleotide Polymorphism) containing gaps and errors. Mutated form of human genome is responsible for genetic diseases which mostly occur in SNP sites. In this paper, a fuzzy clustering approach is performed for haplotype reconstruction or haplotype assembly from a given sample Single Nucleotide Polymorphism (SNP). In the best previous approach based on reconstruction rate (Wang 2007[2]), all SNP-fragments are considered with equal values. In our proposed method the value of the fragments are based on the degree of membership... 

Gene proliferation is vital for infectious and genetic diseases diagnosis from a blood sample, even before birth. In addition, DNA sequencing, genetic finger-print analyzing, and genetic mutation detecting can be mentioned as other procedures requiring gene reproduction. Polymerase chain reaction, briefly known as PCR, is a convenient and effective way to accomplish this task; where the DNA containing sample faces three temperature phases alternatively. These phases are known as denaturation, annealing, and elongation/extension which in this study -regarding the type of the primers and the target DNA sequence- are set to occur at 95, 58, and 72 degrees of Celsius. In this study, a PCR device... 

Haplotype information has become increasingly important in analyzing fine-scale molecular genetics data, Due to the mutated form in human genome; SNPs (Single Nucleotide Polymorphism) are responsible for some genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of studies in human genomics. In this paper, a data fusion method based on multiple parallel classifiers for reconstruction of haplotypes from a given sample Single Nucleotide Polymorphism (SNP) is proposed. First, we design a single greedy algorithm for solving haplotype reconstructions. [2] is used as an efficient approach to be combined with first classification method. The... 

Mutations in Single Nucleotide Polymorphisms (SNPs - different variant positions (1%) from human genomes) are responsible for some genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of recent studies in human genomics. Two sequences of mentioned SNPs in diploid human organisms are called haplotypes. In this paper, we study haplotype reconstruction from SNP-fragments with and without genotype information, problems. Designing serial and parallel classifiers was center of our research. Genetic algorithm and K-means were two components of our approaches. This combination helps us to cover the single classifier's weaknesses. ©2008 IEEE