Solving MEC model of haplotype reconstruction using information fusion, single greedy and parallel clustering approaches

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 methods and information fusion approach are aimed specifically for increasing reconstruction rate of the problem in Minimum Error Correction Model (MEC) which is one of haplotyping problem models belonging to NP-hard class. Designing a parallel classifier, which helps us cover the single classifier’s weaknesses, was the focus of our research.