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Differently from survey methodologies, our method affords data collection and behavior characterization at the individual level, and thus may be used to inform agent-based modeling approaches.
In these cases, as with currently available methodologies, our method will fail to provide meaningful conversions.
In this study, instead of using the supervised or unsupervised learning tools as in the Bayesian approach and other methodologies, our method uses a network approach on the combination of the ChIP-chip data and the TFKO data to infer the functional TF-gene binding pairs.
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Contrary to traditional methodologies on eliciting preferences, our method considers the consequences of potential mistakes in decisions.
As validity of methodology, the performance of our method was compared with traditional methods.
Thus our method provides a general methodology for gene interaction enrichment analysis for gene expression data.
If you have any concerns about our methods and methodology which you wish to raise, please email [email protected], putting "review group" in the subject line.
We cannot conclude that differences were owing to our methodology as our PCR and sequencing methods were robust and results repeatable.
Because the arrangements were originally identified from massively parallel sequencing to generate comprehensive genomic profiles, we cannot conclude that differences were owing to our methodology as our PCR and sequencing methods.
Abril et al. [30] classified the capacity methodologies as analytical methods, optimization methods, and simulation methods.
Also the correction is very light because, as explained in Section 3.4, it is based on the resampling of a precomputed Gaussian function.
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