Bioinformatics has been one of the most fast developing
fields in scientific research. As an important tool in bioinformatics,
microarray technology was developed in a great
need. It provides a remarkable methodology to monitor the
expression level of a large amount of genes. This technology
has created a number of new opportunities to study human
disease. However, the resulting mass of data, often as millions
of measurements, can be overwhelming for human comprehension
and pose great challenges on data pre-processing
and analysis methodology. One of the major difficulties with
microarray data is the presence of missing values that are
commonly due to a number of diverse reasons. These include
insufficient image resolution, dust or scratches on the slide or
experimental error during the laboratory process. Generally,
missing of data around 1-10% would affect up to 95% of
the genes in any microarray experiment [3]. Of course, one
possible solution to recover the dataset with missing values is
to repeat the experiment, which is inefficient and sometimes
infeasible. Henceforth, the missing values in the original data
need to be systematically predicted.