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Self-organizing Maps for Missing Value Imputation in Transcriptomic Datasets
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DOI：10.1007/978-3-032-02725-2_11.png)
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Abstract
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This paper proposes an approach to missing value imputation in a transcriptomic dataset using a self-organizing map. The self-organizing map is trained on a complete subset of the data. Instances with missing values are presented to the trained map and the best matching unit is used to impute the missing values in the instance. The empirical results show promise in the application of self-organizing maps for missing value imputation in transcriptomic datasets.
Keywords:
Transcriptomics
missing value imputation
gene expression
self-organizing maps
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