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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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Both of these approaches generates a much larger amount of reads compared to 454 sequencing but at a cost of much shorter reads.
This approach generates an enclosing surface around each hemisphere and computes the 'local' difference in surface between this surface and the pial surface of the cortex.
Unfortunately, in highly dynamic networks, this approach generates an overhead of control packet exchange between the ordinary nodes and the control plane, that leads to additional energy consumptions.
The proposed approach generates an effective and reliable fuzzy logic control system by powerful searching and self-learning adaptive capabilities of GA.
This conservative approach is the reason why our approach generates an inner bound on the SPCGS rate region rather than the SPCGS rate region itself, but it is also the key to the computational efficiency of the algorithm.
These approaches generate a flurry of activity and might even improve a company's short-term results.
On the other hand, wrapper approaches generate a set of candidate features by adding and removing features to compose a subset of features.
The 16 configurations were modelled with two different approaches: generating a new mesh for each trim condition (standard method) and using a morphed version of the baseline condition.
While semi-automatic approaches generate a useful scaffold for a consensus network, the resulting description still requires extensive manual curation.
DNA-Seq approaches generate a large number of variants and often secondary filtering is required to prioritize the candidate disease genes.
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CEO of Professional Science Editing for Scientists @ prosciediting.com