Finally, we combined these two approaches and labeled groups of similarly-spiking cells in correlograms of electrophysiological values that were found to be best linear predictors for cell spiking output, as described above.
In more recent studies, however, it is found that TLC may not be a reliable predictor for CD4 cell count in HIV-infected individuals in certain settings [ 35, 36 ], while it is reliable in others [ 37 – 41 ].
To assist the development of peptide-based diagnostics, therapeutics and vaccines, Wee et al. [ 33] have developed a novel predictor for B cell epitopes, while Yoo et al. [ 34] address the issue of predicting AIDS disease progression using HIV structural gp120 profiles.
Next, we examined the robustness of the eight-gene predictor, for classifying cells into the enzastaurin-sensitive group, in an independent set of NSCLC cells, and found that the eight-gene predictor correctly classified all five resistant cells (Table 2).
Furthermore, design a predictor for B-cell epitopes, which have high variable epitope length, is more complex than predictor for T-cell epitopes [ 14].
To train a predictor for T-cell reactivity, a large dataset of peptides and their immunogenicity is needed.
Furthermore, five transcriptional biomarkers were found to be good predictors for CD4-/CD8+ countsounts, IL2 production, or phagocytic activity.
BMI showed intercorrelation with MUAC, but MUAC proved to be a stronger predictor for low CD4 cell counts.
To deduce these processes, we looked for basic intrinsic properties that could serve as good predictors of cell spiking output across all experimental groups (220 cells).
