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Finally, this analysis can be repeated by using different prediction methods, which could lead to different results.
According to the results of [15] the overall prediction accuracy increases by using different prediction algorithms for different workloads.
Thus, based on the theoretical and experimental results in this paper, we can accept the main hypothesis that is, the prediction accuracy of time-series techniques is positively impacted by using different prediction algorithms for the different cloud workload patterns.
In our previous study on ischemic injury in the rat gracilis muscle, we observed increased expression of miR-21 during IRI and identified 4 miR-21 potential target genes (Nqo1, Pdpn, CXCL3, and Rad23b) by using different prediction algorithms and monitoring miRNA and mRNA expression at different time points on a genome-wide basis [ 47].
This study has profiled an increased expression of miR-21, miR-200c, and miR-205 in the gracilis muscle following ischemic injury and identified four potential target genes (Nqo1, Pdpn, CXCL3, and Rad23b) of the miR-21 by using different prediction algorithms and monitoring the expression of miRNA and mRNA at different time point on a genome-wide basis.
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Signalpeptide prediction using different prediction algorithms.
We investigated this region for coding sequences manually and by using different gene prediction software such as FGENESH (www.softberry.com).com
These models were constructed by using different statistical learning methods, yielding different prediction capability with Rtest ranging from 0.5 to over 0.9.
It is recommended to increase confidence in prediction by using different types of data (e.g., in vitro and in vivo, more than one scaling method, and information from other compounds with similar properties).
Metabolomics and genomics data are compiled by using different computational tools or databases for identification, prediction, analysis, and biosynthesis process development of secondary metabolites.
For varying posterior probability cutoffs, the ROC (Receiver Operating Characteristic) curves of our predictions were generated by using different combinations of the three types of evidence as shown in Figure 2.
More suggestions(16)
by using different carrier
by using different diffusion
by using different color
by using different weight
by using noncausal prediction
by using different adjuvant
by using causal prediction
by using different parameter
by using different digestion
by using different induction
by using different test
by using different combination
by using standardized prediction
by using different background
by using essential prediction
by using bioinformatic prediction
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