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Study on Classification Methods in Genome Wide Association

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Genome wide association studies have proved that genetic variants would increase the risk to common and complex diseases. The studies produced a lot of single nucleotide polymorphisms data but many variants are still in mystery and yet to be discovered. Existing researches have found tremendous findings on computational intelligence methods in identifying loci, reducing dimensionality and also detecting and modeling gene–gene interaction in certain diseases. However, these methods could be improved in serving certain purposes. Therefore, this paper would like to briefly discuss on the classification method, which is a part of computational intelligence methods in genome wide association studies and facilitate researchers with related studies in the development of machine learning algorithms. It is our intention to further investigate in improving the algorithms in foreseeable future.
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Document Type: Research Article

Publication date: November 1, 2013

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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