Abstract
DNA microarray usage in genetics is rapidly proliferating, generating huge amount of data. It is estimated that around 5-20% of measurements do not succeed, leading to missing values in the data destined for further analysis. Missing values in further microarray analysis lead to low reliability, therefore there is a need for effective and efficient methods of missing values estimation.
This report presents a method for estimating missing values in SNP Microarrays using k-Nearest Neighbors among similar individuals. Usage of preliminary imputation is proposed and discussed. It is shown that introduction of multiple passes of kNN improves quality of missing value estimation.
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Podsiadly, P. (2014). Estimation of Missing Values in SNP Array. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8482. Springer, Cham. https://doi.org/10.1007/978-3-319-07467-2_45
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DOI: https://doi.org/10.1007/978-3-319-07467-2_45
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07466-5
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