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Machine Learning-Based Imputation of Missing SNP Genotypes in SNP Genotype Arrays

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Computational Medicine in Data Mining and Modeling

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Abstract

The missing value problem in SNP genotype data sets is introduced along with a short overview of two commonly used imputation algorithms, fastPHASE and KNNimpute, used to resolve the missing value problem for such data sets. A comparison of the two algorithms is provided with additional preliminary biological and mathematical background information for a better understanding of the problem and the algorithms mentioned.

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Notes

  1. 1.

    The agents of evolutionary change are mutation, genetic drift, sexual selection, natural selection, and gene flow.

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Authors and Affiliations

  1. Mathematical Institute of the Serbian Academy of Sciences and Art (MISANU), Belgrade, Serbia

    Aleksandar R. Mihajlovic

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  1. Aleksandar R. Mihajlovic
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Correspondence to Aleksandar R. Mihajlovic .

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Editors and Affiliations

  1. Mathematical Institute, Serbian Academy of Science and Arts, Belgrade, Serbia

    Goran Rakocevic

  2. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Tijana Djukic

  3. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Nenad Filipovic

  4. School of Electrical Engineering, University of Belgrade, Belgrade, Serbia

    Veljko Milutinović

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Mihajlovic, A.R. (2013). Machine Learning-Based Imputation of Missing SNP Genotypes in SNP Genotype Arrays. In: Rakocevic, G., Djukic, T., Filipovic, N., Milutinović, V. (eds) Computational Medicine in Data Mining and Modeling. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8785-2_6

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  • DOI: https://doi.org/10.1007/978-1-4614-8785-2_6

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