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Using this test, we confirmed the absence of synergy between the 2 protective factors in the SR population (synergy factor = 1.3 [95% CI: 0.37 4.75], Psynergy =.6).
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Because the synergy factor can uncover unexpected synergies, we determined this statistic for the EU population in comparison with the chronically infected individuals.
Visible light absorption capability of both CN and doped TiO2, complements the synergy factor.
Interpretation of the results is straightforward: (1) the closer the calculated synergy factor is to 1, the stronger the synergy between the two (or three) variables can potentially be; (2) the closer the ratio is to 1, the stronger is the potential for a trade-off; and (3) when the synergy factor is close to 0, there is delinking between the trends.
For patients who spontaneously resolved infection, IL28B and KIR:HLA protected, independently, against chronic HCV infection, based on logistic regression and synergy analyses (synergy factor, 1.3; 95% CI: 0.37 4.75; P synergy =.6).
11 genes (blue segment in Figure 1C; Nos2, Il6, Ccl7, Ccrl2, Cd83, Edn1, Il10, Il1rn, Il27, Slamf1, and Slc28a2) showed a microarray synergy factor of >2, and 28 genes (orange segment) showed synergy factors between 1- and 2-fold.
The authors were only able to support 27 of the originally reported gene-gene interactions using their synergy factor analysis.
Tests for epistatic interaction were performed using the Synergy Factor Excel spreadsheet made available by Cortina-Borja et al. (2009).
Additionally, the synergy factor is designed to be robust for small samples sizes, even when individual cells are zero.
Synergy factors are an extension of logistic regression and for the purposes of this discussion are included in that group.
Return of the Population Growth Factor.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com