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An Accurate and Robust Missing Value Estimation for Microarray Data: Least Absolute Deviation Imputation

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2 Author(s)
Cao, Y. ; Dept. of Ind. & Syst. Eng., Nat. Univ. of Singapore ; Poh, K.L.

Microarray experiments often produce missing expression values due to various reasons. Accurate and robust estimation methods of missing values are needed since many algorithms and statistical analysis require a complete data set. In this paper, novel imputation methods based on least absolute deviation estimate, referred to as LADimpute, are proposed to estimate missing entries in microarray data. The proposed LADimpute method takes into consideration the local similarity structures in addition to employment of least absolute deviation estimate. Once those genes similar to the target gene with missing values are selected based on some metric, all missing values in the target gene can be estimated by the linear combination of the similar genes simultaneously. In our experiments, the proposed LADimpute method exhibits its accurate and robust performance when compared to other methods over different datasets, changing missing rates and various noise levels

Published in:

Machine Learning and Applications, 2006. ICMLA '06. 5th International Conference on

Date of Conference:

Dec. 2006

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