Abstract
In DNA and RNA, five types of nitrogenous bases are present; these are adenine (A), uracil (U), guanine (G), thymine (T), and cytosine (C). Sometimes, these arrangements are altered which is known as single nucleotide polymorphism. These polymorphisms appear due to two causes (i) mutation and (ii) disease. By classifying these two types of SNPs, we can conclude a disease-causing SNP. In this paper, we describe genetic analysis of simple and complex disease evaluation by various methods. Here, we describe Apriori algorithm, genetic algorithm, machine learning approach like support vector machine (SVM). An approach of Apriori-Gen algorithm is also discussed which evaluates statistical interaction between several SNPs to find association among them. Univariate marginal distribution algorithm (UMDA) and support vector machine (SVM) are also used to find disease identification. USVM is used not only for its redundancy feature but also it solves parameters selecting problem of SVM.
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Panigrahi, J., Mishra, B.S.P., Dash, S.R. (2019). Disease Prediction on the Basis of SNPs. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 814. Springer, Singapore. https://doi.org/10.1007/978-981-13-1501-5_56
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DOI: https://doi.org/10.1007/978-981-13-1501-5_56
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