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Although several predictive models have been reported for predicting protein oligomerization, however no computational studies exists for specifically analyzing and investigating FPs.
A number of analytical models have been reported for predicting the gas production from gas hydrates.
Though many promising results have been reported for predicting prices, selecting stocks, and managing assets using machine-learning techniques, considering all of them is challenging because of their complexity.
Several machine learning methods have been reported for predicting DNA-binding residues in protein sequences.
However, only a few serum markers have been reported for predicting fibrosis progression and regression during treatment.
Previlously, good reliability and validity of the WS had been reported for predicting psychological problems in patients with chronic pain (Novy et al. 1998).
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The PNN model presented in this paper complements the recurrent neural network model developed by the authors previously, where good results were reported for predicting earthquakes with magnitude greater than 6.0.
Recently, a similar case of very low rate in target validation was reported for predicted targets of new miRNAs identified in Arabidopsis lyrata [ 47].
For instance, seven alternative RNA forms have been reported for rbp9, which are predicted to encode six distinct polypeptides.
This has earlier been reported for serum triglycerides which better predicted cardiovascular risk when assessed in a non-fasting state [ 24].
High AUC (0.866) has also been reported for the Framingham risk score model in predicting CVD death in Australian men and women [ 60].
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