Random forest analyses together with leave-1-out cross-validations across all possible combinations were applied to determine the prediction accuracy and relative importance of each factor for classification.
Finally, a total of 2,944 primer combinations were applied in this study.
Similarly, HM features, TF binding features, and HM+TF feature combinations were applied to linear regression model, respectively.
The following combinations were applied: anti-CD3/anti-CD4, anti-CD3/anti-CD8, ANNEXIN-V/anti-CD4/anti-CD3NEXIN-V/ANNEXIN-V/anti-CD8/anti-CD3V/ANNEXIN-V/antiCD14ANNEXIN-V/antiCD14CD14.
The following combinations were applied: anti-CD3 FITC)/CD4(PE), anti-CD3 FITC)/CD8(PE), anti-CD3 FITC)/CD(16+56)(PE), anti-CD3 FITCC), anti-CD3 FITCC)/CD4(PE)/PI anti-CD3 FITCnexin-V(FITC)/CD 16+56(PE)/PI(PC5).
After incubating the cells with the inhibitors for 45 minutes, the same stimulus/inhibitor combinations were applied and following overnight incubation we removed the supernatant and measured the secretion of 33 cytokines.
The results relating to convex combinations are applied to quasi-arithmetic means.
Three regularization methods and their combinations are applied to improve the robustness of the eigenphone-based method.
In the case of STECCs, linear combinations are applied on binary elements which greatly relaxes the constraints to achieve full diversity STECCs.
In total, six feature enhancement, or front-end processing, combinations are applied in the evaluation; DM alone, NMF alone, DM-initialized NMF (denoted by DM+NMF), and MDI-initialized NMF (denoted by MDI+NMF).
