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The GRAMM methodology is specially suited to dock protein models because it averages over possible errors in the conformation that are unquestionably larger than in the case of crystallographic structures [ 55, 63, 64]. Figure 1 shows the four highest scores of interaction for each of the relevant genes.
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In addition, we propose a non-parametric methodology that is specially tailored for analyzing the productive efficiency of research: it starts from a specification of the managerial objectives of research activities while imposing minimal structure on the (typically unknown) production technology.
The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems.
The last is specially significant.
This is specially the case in the year 2007, when a turning point was estimated in activity, the imbalances were at historical highs and our methodology signals a huge output gap, both ex-ante and ex-post.
This is specially visible in regions where SNR is lower.
Our software O18Quant is specially designed for O labeled data.
Like Dørum et al. (2009), we use rotation to evaluate P-values, but our methodology is for self-contained tests with specially targeted gene sets, whereas they considered competitive tests between a large database of gene sets.
This methodology is applied to test data obtained by a specially-designed bread-board cell.
Her methodology is simple.
"Your methodology is wrong".
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