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Updated: Jan 22, 2026

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
Published on: April 14, 2010
Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify
Han-Ming Liu1, Dan Yang1, Zhao-Fa Liu1
1School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China.
Abstract:
The hypothesis of data probability density distributions has many effects on the design of a new statistical method. Based on the analysis of a group of real gene expression profiles, this study reveal that the primary density distributions of the real profiles are normal/log-normal and t distributions, accounting for 80% and 19% respectively. According to these distributions, we generated a series of simulation data to make a more comprehensive assessment for a novel statistical method, maximal information coefficient (MIC). The results show that MIC is not only in the top tier in the overall performance of identifying differentially expressed genes, but also exhibits a better adaptability and an excellent noise immunity in comparison with the existing methods.
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