Imputation Technique for Feature Selection in Microarray Data Set

Analyzing DNA microarray data sets is a great
challenge, which faces the bioinformaticians due to the complication
of using statistical and machine learning techniques. The challenge
will be doubled if the microarray data sets contain missing data,
which happens regularly because these techniques cannot deal with
missing data. One of the most important data analysis process on
the microarray data set is feature selection. This process finds the
most important genes that affect certain disease. In this paper, we
introduce a technique for imputing the missing data in microarray
data sets while performing feature selection.

• Younies Mahmoud
• Mai Mabrouk
• Elsayed Sallam

• DNA microarray
• feature selection
• missing data
• bioinformatics.

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