Let μ T, G (1 ) and μ T, G (0 ) be the group means corresponding to the genotypes G i. To test the hypothesis H 0 : μ T, G (1 ) = μ T, G (0 ), a few test statistics are proposed for microarray data analysis[ 26].
Although different statistical approaches have been proposed for analyzing microarray time-course data, method for analyzing such data collected using the popular case control design in clinical investigations has not been proposed perhaps due to the increased complexity for the existing parametric or non-parametric approaches.
Different analytical tools have been proposed for analysing microarray data (Eisen et al, 1998; Golub et al, 1999; Kalocsai and Shams, 2001; Tamayo et al, 1999; Toronen et al, 1999; Bittner et al, 2000).
Moreover, as an alternative to using the overlap size, several statistical approaches have been proposed for comparing lists of genes from microarray experiments [34], [35].
A link between carbon and nitrogen metabolism, PTS and two-component regulatory systems have been proposed for some bacteria [18], and our microarray results strongly suggest that same relationship could be made for Brucella.
Statistical approaches have been proposed for ratiometric data to improve inter-microarray variations, especially of non-linear relations [ 10].
Many methods have recently been proposed for reverse engineering of gene networks by using gene transcript expression data measured by microarray.
This has been proposed for histone acetylation.
In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection.
