Abstract
In this paper, we address the problem of locating a disease gene using a new Haplotype Segment Algorithm based on distance/similarity measures. We developed a novel approach to identify the set of associated marker alleles in the form of haplotype segments. We measure the distance of haplotype segments in cases and controls. We find the two haplotype segments in cases which have least distance than controls and observe that the disease location lies between these two haplotype segments. Haplotype Segment Algorithm performs the similarity analysis of haplotypes and finds the location of a disease gene using various distance/similarity measures. Haplotype Segment Algorithm uses the similarity or distance measures so this algorithm has the capability to reduce or delete the noise from the segments and finds a precise location of disease gene. The new algorithm detects the disease gene even if there exists 5% mutant chromosomes in the human genome. We applied new algorithm on simulated datasets and find the location of disease gene very close to the true simulated location. We also assessed the performance of Haplotype Segment Algorithm on a real dataset called Friedreich Ataxia’s dataset in detecting a disease gene location and find the consistent results.
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Ahmed, A., Saleem, K. (2012). Haplotype Segment Algorithm for Predicting Disease Gene Locus Based on Distance/Similarity Measures. In: Chowdhry, B.S., Shaikh, F.K., Hussain, D.M.A., Uqaili, M.A. (eds) Emerging Trends and Applications in Information Communication Technologies. IMTIC 2012. Communications in Computer and Information Science, vol 281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28962-0_4
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DOI: https://doi.org/10.1007/978-3-642-28962-0_4
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