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This score was normalized in a similar way as previously described, where the initial known modules were defined using the minimal co-regulation module definition and subsequently randomly permuted by mapping the genes within the modules to a random permutation.
We first used a grid search to explore the parameter space of every method and determine their most optimal parameters given a certain dataset and module definition, which resulted in a training scores.
We finally calculated the harmonic mean between the normalized versions of all four scores to obtain a final score representing the performance of a particular method on a given dataset and module definition.
These are pre-defined product advantage, selectively used design rules, module definition, system integration, technological newness, internal communication, and supplier and customer involvement.
The entire application is written in JavaScript according to ECMAScript 6 which offers classy, flexible and cleaner syntax, a universal way for module definition, etc. Chemozart transpiles ES6 codes to ES5-friendly codes using Babel https://babeljs.io/ for browser support.
DCL has the following positive points: (i) expressiveness, DCL supports a wide variety of architectural constraints; (ii) abstraction level, DCL provides a limited support to associate specific source code parts (low level) with abstract elements (high level) by using a module definition; and (iii) applicability, once it is an easy to learn language.
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For every gold standard, we obtained sets of known modules based on three different module definitions.
The original multichip module definitions are based on categorization by their substrate material.
To avoid certain gold standards and module definitions from disproportionately influencing our final score, we normalized each score using random permutations of the known modules.
Performance was generally very consistent across different module definitions (Fig. 2d and Supplementary Fig. 3b), despite limited similarity between the sets of known modules (Supplementary Fig. 5).
We found that all three main module definitions generate modules which are both more globally and more locally (according to extreme expression biclustering definition, see Supplementary Note 2) co-expressed compared with permuted modules (Supplementary Fig. 19).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com