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In an effort to reduce the complexity of the interactions contained in the network modules we constructed consensus networks based on the most abundant HLA proteins for each risk group and only included partners outside the HLA with a stringent criteria (see Methods).
To cover the different sizes of these modules, we constructed five controls, four of which are composed of the gene sets with an invariable size, the number of genes in each gene set in the four controls ranging from 20 to 50 in ascending order; while the fifth control consisted of the gene sets with variable size from 20 to 50 which was randomly determined.
To visualize the correlations between probes within the modules we constructed coloured heatmaps by plotting pair-wise correlation values of expression of all the probes within the modules.
For each of these modules we constructed a nearest mean classifier using the genes common between the enlarged signatures and the group of genes in the module.
To investigate the effect of force on CD4 modules, we constructed a polyprotein composed of domains 1 and 2 of CD4 bracketed by dimeric handles made of the 27th domain of human cardiac titin used as molecular fingerprint, (I27 2-CD4D1D2- I27 2.
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To determine the distance over which cooperative interactions can be propagated in a repeat protein, and to investigate the tolerance to internal duplication and deletion of modules, we have constructed a series of ankyrin repeat variants of the Notch ankyrin domain in which repeat number is varied by duplication and deletion of internal repeats.
The results of our automatic search provide an elaborate network of such inter- and intra-module edges, thus, we constructed a web-tool providing a user-friendly interface to browse our results in an effective manner (Supplementary website).
We constructed modules from all three algorithms using default parameters (the module creation is independent of any expression data).
For each of the validation schemes (Table 1) we constructed several module-based classifiers (BC, BCC, HCC, S456) based on modules extracted from the datasets indicated in the Features column of Table 1.
To identify the influence of patient features (array or experimental sets) on gene information (gene sets), we constructed several module maps, stratifying patients according to demographic, clinical, laboratory and therapeutic characteristics.
We constructed glass RPC modules for detecting KL and muon in BELLE experiment.
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