The objective of the optimization is to determine the set of model parameters, Θ, that optimally explains the input data, D. Θ depends on the thermodynamic model.
After termination of the EM algorithm, the mixture optimally explains the data in terms of having generated it.
The goal of our optimization problem is to infer the activity of TFs as a function of cultivation parameters, such that we can optimally explain the regression coefficients, which were distilled from the observed gene expression data.
The rooted rings that optimally explain the distribution of both the genes and indels, shown in figure 3, are slightly more complex as they accommodate an additional indel in the GGGPS gene that is present in the Eocyta and is absent in the Halo bacteria.
The algorithm deciphers the minimal set of mutational signatures that optimally explains the proportion of each mutation type found in each cancer sample and then the method estimates the contribution of each signature to each cancer sample (see Ref. [ 20 ] for more details about this method, including a discussion of its limitations).
To reduce the complexity of the system and identify the most plausible candidate cytokines involved in regulation of osteoclastogenesis, we developed a PLSR model, which aims to extract the linear combinations of the predictors (factors in the signal dataset), which optimally explains the variation in response (osteoclastogenesis outcomes).
In this study, we use the TFInfer approach (4, 65), a Bayesian statistical method that integrates gene expression data with regulon information (collected from online databases such as Regulon DB or Ecocyc) to identify transcription factor (TF) activity profiles that optimally explain the measured changes in gene expression.
The collective effect of 12,627 SNPs with Cochran-Mantel-Haenszel test (p < 0.2) in our discovery GWAS set optimally explains ∼3% of the variance in MS risk in our independent target GWAS set, estimated by Nagelkerke's pseudo-R2.
The issue addressed in this paper is to identify the best site parameters that optimally explain, and therefore predict, the actual site-specific amplification factor.
We then compared combinations of bilinear and nonlinear dynamic causal models (Friston et al. 2003) to identify connectivity models that optimally explain how interactions between these form and motion pathways could generate STS responses to dynamic faces.
