UDC 004.423, DOI: 10.2298/csis0902165H

Mou'ath Hourani¹ and Ibrahiem M. M. El Emary²

¹ Faculty of Information Technology, Al Ahliyya Amman University, Amman, Jordan
Mouath.hourani@gmail.com
² Faculty of Engineering, Al Ahliyya Amman University, Amman, Jordan
omary57@hotmail.com

Abstract. Gene expression data often contain missing expression values. For the purpose of conducting an effective clustering analysis and since many algorithms for gene expression data analysis require a complete matrix of gene array values, choosing the most effective missing value estimation method is necessary. In this paper, the most commonly used imputation methods from literature are critically reviewed and analyzed to explain the proper use, weakness and point the observations on each published method. From the conducted analysis, we conclude that the Local Least Square (LLS) and Support Vector Regression (SVR) algorithms have achieved the best performances. SVR can be considered as a complement algorithm for LLS especially when applied to noisy data. However, both algorithms suffer from some deficiencies presented in choosing the value of Number of Selected Genes (K) and the appropriate kernel function. To overcome these drawbacks, the need for new method that automatically chooses the parameters of the function and it also has an appropriate computational complexity is imperative.