The good consistency observed in E. coli (the Jaccard coefficient, 0.60 in total. See Table 3) could be due to the similarity of methodologies that detect minimal pathway maps and persistent genes.
Despite spanning almost half a century, the research on this subject has proven a lack of globally applicable damage detection methodology that can detect damage without the use of parameters from the undamaged state.
Given these considerations, our goal was to find a cost-effective and high throughput methodology that would detect frequent and infrequent cancer mutations genes in a large number of samples.
To perform this comparison using heterogeneous datasets from different measurement platforms and organisms, we developed a novel non-parametric methodology that can detect statistical significance of overlaps in ranked lists of genes, and estimate the number of genes with a common expression profile.
First, it is of the utmost importance to design an experimental protocol so that the cognitive variable of interest may be analyzed unambiguously, and second, it is necessary to apply an appropriate methodology that is able to detect and quantify the relevant neural activity.
This elegant methodology that allows one to detect halide concentrations in the range of 1-500 mM is restricted to the samples where halide salts are absent since they quench the fluorophore.
We propose a new methodology that is able to detect such higher order relationships.
This paper focuses on specific aspects of a new framework including techniques and methodologies that can detect significant block level errors that lead to spectral degradation.
Although bioorthogonal chemistry provides a promising platform to study the lignification process, methodologies that simultaneously detect multiple chemical reporters in living organisms are still scarce.
