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To this end, we focused on mouse genes because of availability of microarray data for mouse embryonic development.
Dimension reduction is a critical issue in the analysis of microarray data, because the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers.
Because of the asymmetric distribution of microarray data, expression values were log2 transformed.
This is a substantial property in the analysis of microarray data, because the understanding of the biological pathway may be improved by the identification of an entire set of correlated genes [5].
Imputation of missing values is a very important aspect of microarray data analyses because most of the downstream analyses require a complete dataset.
Imputation of missing values is a very important aspect of microarray data analyses because most of downstream analyses require a complete dataset.
GeneSifter™ is unique in the field of microarray data analysis software because it is the only package that is wholly web-based http://www.geospiza.com/Products/AnalysisEdition.shtml.shtml
The interpretation of microarray data remains a major challenge because of the complexity of the underlying biological networks.
This is because variations in gene dosage can complicate the interpretation of microarray data and may also differentially contribute to the developmental or tumour phenotype [13].
This result is significant because the correlation coefficient is the generally accepted criterion for assessing reliability of microarray data [ 11].
Because many factors such as RNA isolation, labeling and hybridization methods affect the quality of microarray data, pooling data from different experiments does not allow for a rigorous expression profiling analysis.
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