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We applied the EgoNet algorithm in conjunction with the three classifiers for subnetwork selection, and inspected if the top identified ego-netowork (s) recovered the true subnetwork.
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By this, we ensure a stringent selection of subnetworks for further analysis: Resulting subnetworks are both enrichted with hits from the RNAi screens, and show topological properties that distinguish them from random clusters.
In combination, these criteria resulted in a stringent selection of subnetworks, compare Table 1.
Import of a BioPAX file, selection of a subnetwork of interest saved as a SBML file for the creation of a computational model using an appropriate software package, such as CellDesigner [ 20] or GINsim [ 32].
Specific functionality includes: (i) selection of arbitrary subnetworks using a graphical editor provided by Cytoscape; and (ii) defining special interest networks based on network motifs, cycles, modules, shortest paths determined by existing Cytoscape plug-ins, such as SubgraphCreator.
Both simulation and real genomic data studies showed that the enhanced L1/2 net is a ranking procedure compared with L1/2 net (using the old thresholding operator), L1 net, and the Elastic net in the selection of biomarker and subnetwork.
It does not prohibit the selection of several disconnected subnetworks.
S is only selected if we have at least | S|/2 genes that are not from L. We finish the selection process with 50 subnetworks.
For example, the EAS subnetwork formed by the candidate genes under positive selection showed some interesting patterns.
First, we extracted the subnetworks that were composed of candidate genes of recent positive selection.
If selection pressures act on an entire pathway or a functional subnetwork, multiple genes in the same pathway/subnetwork may change in the same fitness direction, and at a same evolutionary rate and time to achieve a common phenotypic outcome.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com