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Both of these approaches generates a much larger amount of reads compared to 454 sequencing but at a cost of much shorter reads.
Since each of these five miRNA target prediction approaches generates an unpredictable number of false positives (an estimate puts it at 20 30% [ 29]), we intersected the results to identify the genes commonly predicted by at least two of the five algorithms [ 8, 9, 26- 28] (see Table 1; for a complete list, see Additional file 3).
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These approaches generate a flurry of activity and might even improve a company's short-term results.
Many different approaches generating C1-smooth surfaces are available and well-studied.
Those who favour the former suggest that new approaches generate models that are too complex and not sufficiently understandable.
Both approaches generated very similar AUC values.
All processing approaches generated slightly different outputs.
Informatics approaches generate hypotheses, not conclusions [ 57, 58].
However, two multifactor analytical approaches generated different results.
Such approaches generate comprehensive and in-depth understanding.
All these multidisciplinary approaches generate huge datasets which can only be analyzed with an automated approach.
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