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Under certain circumstances, this method can fall within generally accepted accounting principles.
Some business people who are used to the traditional waterfall method can fall into an unfortunate cycle: taking months to develop requirements, then waiting for IT to respond, then telling IT that's not what they wanted.
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The experiment results show that our method can achieve 90.06% and 85.91% prediction accuracy with 94.37% and 83.33% specificity at the sensitivity of 85.74% and 85.27%, respectively.
When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs.
The compared performances are given in Fig. 13 a, b on DIC microscope images of one wild-type embryo which shows that our proposed method can give 80∼90 % dice coefficients and 70∼90 % AO values even for two sides of images while the local entropy-based method failed for two sides of images despite the similar detection performances to our proposed method around the middle Z-slice.
The negative predictive value of DHPLC coupled with MLPA is 99.87% and the combined method can detect 93.88% of carriers since most of the cases result from a common single deletion event [40].
The 'dynamic' method can identify 1337 strings in piRNAs and non-piRNAs with a significant level of 10−300, while 'static' method can only identify 21 position-specific base usages with a significance level of 10−100.
Preliminary experimental results show that the throughput of this method can be over 1000 mm2 minute with a brush of 100 mm diameter.
The method can process approximately 1000 conformations per second on a modern computer.
We also see that the proposed extrapolation method can outperform the 0.632+ bootstrap in terms of RMSE when sample size (online supplementary appendix 6).
Some new and related results obtained via the topological degree method can be found in [38 42].
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