Abstract
Microarray experiments normally produce data sets with multiple missing expression values, due to various experimental problems. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene expression values as input. Therefore, effective missing value estimation methods are needed to minimize the effect of incomplete data during analysis of gene expression data using these algorithms. In this paper, missing values in different microarray data sets are estimated using different partition-based clustering algorithms to emphasize the fact that clustering based methods are also useful tool for prediction of missing values. However, clustering approaches have not been yet highlighted to predict missing values in gene expression data. The estimation accuracy of different clustering methods are compared with the widely used KNNimpute and SKNNimpute methods on various microarray data sets with different rate of missing entries. The experimental results show the effectiveness of clustering based methods compared to other existing methods in terms of Root Mean Square error.
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Bose, S., Das, C., Chakraborty, A., Chattopadhyay, S. (2013). Effectiveness of Different Partition Based Clustering Algorithms for Estimation of Missing Values in Microarray Gene Expression Data. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_5
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DOI: https://doi.org/10.1007/978-3-642-31552-7_5
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