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(For an extensive comparison and benchmarking, see the [22].) Note that SignaLink uses more than 20 reviews per each pathway to list pathway components, in contrast to the average 5 15 reviews per a pathway in other resources [22].
This analysis result is in accord with the above cellular compartment distribution, disease gene list, pathway enrichment and functional category enrichment analysis results.
In order to mine the biological information in the final gene list, pathway analysis was conducted to map these genes to known pathways (using Gene Ontology terms as proxy).
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We believe that SignaLink 2 could serve as a systems-level resource to list pathway-specific and mutual miRNAs as well as cross-talks on the post-transcriptional level.
If the input is a compound list, pathways associated with target compounds in KEGG database will be listed.
We have also listed pathway analyses using Panther and MetaCore on the core network proteins.
Table 5 lists pathways using the second method, based on the standard deviation between differentially expressed genes in a given pathway (Top differentially-affected pathways).
The difference in the amount of differentially expressed genes, distinctive gene expression patterns, specific regulated gene lists, pathways and unique gene networks, suggest a specific molecular mechanism for each compound.
Tables listing pathway-based functional annotation statistics.
These listed pathways largely overlap those listed in Table 2.
Table 3 lists pathways that contain most differentially expressed genes when comparing MSCs and osteosarcoma.
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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