Study on Classification Methods in Genome Wide Association
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.
Document Type: Research Article
Publication date: 01 November 2013
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