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Using numerical simulation methods, the prediction accuracy of the two ANN models and the performance of the algorithm were tested.
Among these methods, the prediction performance of naïve Bayes classifier established here displayed very well and stable, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set I set were 89.1 ± 0.4% and 77.3 ± 1.5%, respectively.
We found that the performance of both models varies widely across our population of cells; on this moderately small timescale (predicted and actual responses were computed using 3 ms time bins and were smoothed with a 5 ms Hanning window; see Methods), the prediction correlation was as high as 0.77 for some neurons and below 0.1 for others.
When compared with the traditional methods, the prediction with the application of data mining techniques offered interesting results.
Since BP-C3 outperformed other combination strategies and MS had optimal performance among single feature methods, the prediction capacity between MS and BP-C3 was then compared.
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By using the RFE method, the prediction accuracies are further improved.
In this method, the prediction value of the current sample was the mean of its two closest samples.
To verify the effectiveness of the presented method, the prediction experiment is carried out.
For the DCN method, the prediction completely relies on the Pfam annotation.
Fourth, although we used a popular mnemonic method, the prediction of difficult airways might have been subjective.
The results demonstrated that our method outperformed those previous methods in the prediction of RNA-binding proteins.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com