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The familiar relationships between miRNAs were based on miRBase version 17. TargetScan 4.2 [ 23– 25] was used to predict mRNA targets.
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The construction of the linkages between genes and miRNAs was based on the targeting information from MicroCosm Target database (http://www.ebi.ac.uk/enright-srv/microcosm/htdocs/targets/v5/).
Selection of miRNAs was based on the top 75 miRNAs (based on fold change) differentiating between basal and luminal cancers and between BRCA1 and sporadic basal cancers (Fig. 1).
As the binding between mRNA and miRNA is based on complementary base pairing, a single nucleotide polymorphism (SNP) in the 3′-UTR or seed region would affect the binding efficiency, which may lead to change of the expression level of their target genes [ 12].
Computational approaches to detect miRNAs have been developed to overcome the limitation, and most methods for pre-miRNA detection are based on machine learning [5 7].
Porcine miRNAs were identified based on bioinformatics and based on comparisons to corresponding human miRNA sequences.
The calculation of the p-value for comparison of the miRNA expression between the two libraries was based on previously established methods [ 77, 78].
MiRNA target prediction in plants is based on this high degree of complementarity between miRNAs and targets [ 39].
Of the 63 newly identified miRNAs, 48 began with a 5' uridine, which was a characteristic feature of miRNAs, 11 began with a 5' A, 2 began with a 5' C and G. Names of the seabass miRNAs were assigned based on the homologies between the miRNA and published miRNA sequences in the Sanger database; the isoforms from one family were labeled in alphabetical order (Table S3).
Another limitation is that the identification of modules is based on a negative correlation between miRNAs and mRNAs.
The heatmaps are based on the alignment between TP miRNA-mRNA pairs using 'fasta' sequence alignment tool version 35 [ 74].
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