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- W109080554 abstract "AbstractMissing value imputation for microarray data is important for gene expression analysis algorithms, such as clustering, classification and network design. A number of algorithms have been proposed to solve this problem, but most of them are only limited in linear analysis methods, such as including the estimation in the linear combination of other no-missing-value genes. It may result from the fact that microarray data often comprises of huge size of genes with only a small number of observations, and nonlinear regression techniques are prone to overfitting. In this paper, a quasi-linear SVR model is proposed to improve the linear approaches, and it can be explained in a piecewise linear interpolation way. Two real datasets are tested and experimental results show that the quasi-linear approach for missing value imputation outperforms both the linear and nonlinear approaches.Keywordsmicroarray datamissing value imputationquasi-linearSVR" @default.
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- W109080554 date "2011-01-01" @default.
- W109080554 modified "2023-09-25" @default.
- W109080554 title "A Quasi-linear Approach for Microarray Missing Value Imputation" @default.
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- W109080554 doi "https://doi.org/10.1007/978-3-642-24955-6_28" @default.
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