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Correlation-based biclusters can be acquired by backwardly eliminating less correlated sets of genes and conditions.
Expression-based gene network was built to define clusters of highly correlated sets of genes, called modules.
The KLE for multiple correlated random excitations relies on expansions in terms of correlated sets of random variables reflecting the cross-covariance of the random processes.
Specifically, we conducted ten regressions (suicide attempts: 2, mood disorders: 0, anxiety disorders: 8) and empirically determined whether logistic or additive link functions yielded a better fit to the data, resulting in two highly correlated sets of regressions and a total of twenty tests.
The correlated sets of proteins and side effects were extracted from each component.
This result indicates that separate experiments can identify different but functionally correlated sets of differential genes.
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For instance, when the degree of increase in the APCC of the least correlation condition is higher than that of less correlated set of genes, the former is eliminated and the latter is remained and vice versa.
The correlated set of GIP describes the LLE with an average deviation up to 0.7%, regardless of the type of biodiesel and the temperature (in the range studied).
This paper presents a correlated set of group interaction parameters (GIP) for the UNIFAC model that adequately describe the LLE of such biodiesel systems.
We focus on a circuit endophenotype of the thalamic reticular nucleus (TRN) and how its impairment in neurodevelopmental disorders gives rise to a correlated set of readouts across sleep and attention.
In line with Smith et al [1], we will show that they form a lowly correlated set of signals.
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