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More generally, kinases are an ideal test bed to develop methods for understanding putative disease mutations because abnormally functioning kinases are a major cause of human disease, an extensive body of functional information on kinases exists, and they are a large family and thus well suited for statistical analysis (M anning et al. 2002; L ahiry et al. 2010).
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This is useful, since many statistical methods are well suited for the case where many observations and few variables are available.
This statistical methodology is well suited for extracting the relative weights of the different periodic components of a time series and their variation over time.
All model fitting was performed on raw data with Mx (Neale et al. 2003), a statistical software package well suited for conducting genetic analyses.
The MCMC method is an advanced statistical approach, which is well suited for the analysis of complex multiparametric non-linear models.
Traditional statistical methods are not well suited for detecting such interactions since the number of SNPs and their interactions increase exponentially.
For these reasons, it has been suggested that traditional statistical techniques are not well suited for analyzing drivers of health outcomes and no traditional approach can control all external factors affecting patient health outcomes [ 8].
GP is a non-parametric statistical model that is extremely well suited for modelling HTS time series data, which usually have relatively few time points that may be irregularly sampled.
Compared to single gene analysis methods, pathway-based approaches appear to be well suited for analysis of massive GWAS data, either from biological or statistical considerations [23].
Statistical comparisons of RNA-Seq count data typically use negative binomial models well suited for the over-dispersed counts data characteristic of RNA-Seq [ 49].
However, existing global SH statistical expressions do not represent accurately recent lithospheric field models, and planar geometry is not well suited for large-scale studies (e.g., Langel and Hinze 1998, chapter 5.3.2 or Vervelidou 2013, Figure 5.27b).
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