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To solve this rather complex problem, we propose a novel object localization method by predicting object regions before extracting them.
So far, the researchers have tested their method by predicting the outcome of such events as the American presidential election and the number of books sold on the first day of a Harry Potter release.
We also tested the validation of our method by predicting tissue specific miRNAs and performed a comparison with tissue specific miRNAs identified by biological experiments.
Interestingly, the P method performs similarly to the L method by predicting six new candidates (although only two are common with those predicted by L), while A, S and C methods predict two, two and three additional members, respectively.
This allows us to evaluate our method by predicting gene-article association published after 2009.
Additionally, we also validated our computational method by predicting known miRNAs in A. thaliana and G. max.
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Computational techniques complement laboratory-based methods by predicting highly probable PPIs thereby enabling experimental biologists to focus on fewer interactions and ruling out the vast majority of unlikely ones.
The flutter speed was calculated using the harmonic balance method and by predicting Hopf bifurcation.
Initially, the MV-FRS method starts by predicting the resource (i.e. bandwidth, memory and processing cycle) requirements.
By repeated model tuning, the mechanisms that best explain the observed signals can then be identified, and to some extent such findings can be validated by other diagnostic methods and by predicting follow-up data.
Furthermore, an application of the analytical method is demonstrated by predicting service life of cracked RC pipe piles exposed to marine environments.
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