Query #1 Use a list of genes to check whether they have been significantly gained or lost in the topology breast.
(Kernel Convolution - a Statistical Method for Aberrant Region detection) is a computational approach for statistical analysis of non-discretized aCGH data from multiple experiments, and determines which regions are significantly gained or lost relative to randomized data (P < 0.05) [ 34].
Due to their outstanding successful applications in various areas of interest, nature-inspired and bio-inspired optimization algorithms are significantly gaining attention in nowadays research aspects.
In addition, lower BMI, younger age and weight gain at Weeks 1 through 4 were significantly correlated with gaining at least 7% of baseline weight.
Chromosome 20q was significantly more gained in the LM group compared with the other samples (Kernel Smoothed Estimate (KSE); p < 0.05), Bonferroni multiple testing corrected, Figure 2 top panel, aberrations exceeding the red lines).
Furthermore, chromosome 20q was significantly more gained (20q high; above median of the mean log 2 20q) in the LM group compared with the M0 PM group (KSE, P<0.05, Bonferroni multiple testing corrected) (Bruin et al, 2010).
Instead, subjects are significantly guided by gain-loss frequency under the same final outcome context.
Age, BMI and OHS were significantly associated with QALYs gained (see table 6).
Women who developed SG were significantly younger and had gained significantly more weight during pregnancy compared to those who did not.
Individual contrasts indicated that learning gains for Bio (0.65) and Micro (0.67) were not significantly different (p = 0.421), that Bio normalized learning gains were significantly higher than Tech learning gains (p = 0.002), and that the normalized gain for Micro was higher than the average gains of genetics and genomics (Bio + Tech; p = 0.000).
