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Solving MEC and MEC/GI problem models, using information fusion and multiple classifiers

Asgarian, E ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/IIT.2007.4430390
  3. Publisher: IEEE Computer Society , 2007
  4. Abstract:
  5. 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
  6. Keywords:
  7. Genetic algorithms ; K-means clustering ; Genetic disease ; Genotype information ; Haplotypes ; Human population ; Multiple classifier systems ; Multiple classifiers ; Single nucleotide polymorphisms ; SNP fragments ; Classification (of information)
  8. Source: Innovations'07: 4th International Conference on Innovations in Information Technology, IIT, Dubai, 18 November 2007 through 20 November 2007 ; 2007 , Pages 397-401 ; 9781424418411 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4430390