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Comparison between the computational target prediction and DEGs found only a small portion of the DEGs attributable to direct targeting.
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Figure 7 contains a summary of the results of the computational target prediction.
The computational target prediction algorithm is vital to effective experimental testing.
Compared with the existing computational target deconvolution methods, we also extended the drug target mapping beyond the kinase targets only.
In this report, we present an integrative strategy for inferring HCV-associated miRNA-mRNA regulatory modules, by combining the inverse expression relationships between miRNAs and mRNAs and computational target predictions at the sequence level.
The computational miRNA target searches were performed on the psRNA Target Analysis Server (http://plantgrn.noble.org/psRNATarget/).org/psRNATarget/
MicroRNAs that were differentially expressed between diseased and healthy tissue samples were used for computational target prediction using the bioinformatics tool ComiR.
To predict the genes targeted by miRNAs, two computational target prediction algorithms, TargetScan 5.1 and miRanda, were used [ 51, 52].
In this case, the computational predicted target was not yet validated.
Current computational target prediction methods use only the miRNA sequence as input.
Consequently, computational target prediction is in great demand to restrain the list of putative targets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com