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To obtain a comprehensive description of the folding landscape for a structured RNA, it is necessary to understand the connections between productive folding pathways and pathways to these misfolded states.
A multi-criteria analysis process was used to combine data layers into pathways and pathways into likelihood maps using weightings that reflect the relative importance of each layer and pathway.
Proteins encoded in the genomic sequences form biochemical pathways and pathways join together to form networks.
For these randomly rewired maps, genes were connected to randomly selected pathways, and pathways were connected to randomly selected hallmarks.
Importantly, in the patient data set we identified cancer specific pathways and pathways for basal cell carcinoma.
Similarly, TNF α treatment showed that Metabolic pathways and Pathways in cancer were the top two affected pathways (Table 3).
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We created a multilayered network that includes genes, pathways, and pathway families.
As we did with the manual analysis, we can use our automated approach to discover discriminative pathways and pathway pairs.
Genome-scale oligonucleotide arrays have become a powerful tool in detecting the pathways and pathway interactions underlying biological processes.
We calculated the ARI scores to quantify the similarity of our 195 resultant pathways and pathway annotations from each pathway database (see Section 2).
These pathways include the MAPK signaling pathway, inflammatory response pathway, TGF-β pathway and pathways associated with extracellular matrix synthesis and regulation of gluconeogenesis.
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