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Based on the analysis of graph processes, we have introduced a novel method for the identification of biological modules in ranked relational data.
To reduce the impact of edges that hit a module before birth, we consider parallel graph processes in which each individual high-leverage edge is postponed until the end.
In this graph processes are regarded as nodes, while process interactions are represented as directed edges.
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Thus, all blocks in the flow graph process incoming data chunk-by-chunk [25].
As we increase L, the smaller become the sub-sets of the Tanner graph processed in parallel.
A graph process is a useful representation of pairwise relationships.
In contrast to a single graph, a graph process is an ordered set of graphs generated by incrementing a parameter.
To identify such groups, we analyze a modified graph process: we remove all n−1 module-killing edges individually from the original graph process and apply our method to the modified process, leading to the Miso(1,k) method.
Figure 3 presents an example of a graph process produced by the ranked yeast vesicle transport data.
The study of an evolving graph process offers a promising new direction for the discovery of biological modules.
A graph process is a representation of the ranking of quantitative, pairwise gene gene relationships for n genes.
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CEO of Professional Science Editing for Scientists @ prosciediting.com