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We propose to redress the deficiency by formulating the functional study of biological networks as a control problem of dynamical systems.
In addition to using random networks as a control we carried out several other experiments to test the robustness of our findings and show that they are independent of the way the modules are defined, the amounts of data that are being used, or the orthology matching definitions.
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In particular, the anterior insula/frontal operculum and dorsal anterior cingulate cortex (dACC) form a consistently observed functional network, described as a control network (Dosenbach et al. 2007; Larson-Prior et al. 2009).
We found that the negatively correlated network of interactions among upregulated miRNAs and downregulated mRNAs (average number of neighbors: 2.95, Figure 3A) was no different from randomized networks generated as a control tool (average number of neighbors: 3.03, shown in Additional file 1: Figure S2A).
A user-drawn curve network serves as a control cage, from which a subdivision surface is generated.
The global network serves as a control (Figure 8).
Following this line of reasoning, the suggestion would then be that resting-state cortical activity in the midline regions of the default network acts as a control mechanism for the fixational drift component.
The age of the network is included as a control variable because networks might differ in the different stages of their life-cycles [ 21, 23].
A global inferred network was generated as a control for module-specific relationships, prior to generating the module-specific inference.
In analyzing the distribution of shortest paths between paralogous pairs, an equal number of non-paralogous pairs randomly picked from the network were used as a control.
We plotted the results for different values of δ for the leukemia networks as well as for the control networks.
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